Instrumental Song of Saint Yaredic Chant Derived Automatic Pentatonic Scale Identification: Bagana

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Computer Science thesis

2020-03-17 INSTRUMENTAL SONG OF SAINT YAREDIC CHANT DERIVED AUTOMATIC PENTATONIC SCALE IDENTIFICATION: BAGANA

BAYE, WENDIMU http://hdl.handle.net/123456789/10546 Downloaded from DSpace Repository, DSpace Institution's institutional repository

BAHIR DAR UNIVERSITY

BAHIR DAR INSTITUTE OF TECHNOLOGY

SCHOOL OF RESEARCH AND POST GRADUATE STUDIES

FACULTY OF COMPUTING

INSTRUMENTAL SONG OF SAINT YAREDIC CHANT DERIVED

AUTOMATIC PENTATONIC SCALE IDENTIFICATION: BAGANA

WENDIMU BAYE MESSELLE

Bahir Dar, Ethiopia February, 2019

INSTRUMENTAL SONG OF SAINT YAREDIC CHANT DERIVED

AUTOMATIC PENTATONIC SCALE IDENTIFICATION: BAGANA

WENDIMU BAYE MESSELLE

This Thesis work is submitted to the school of Research and Graduate Studies of Bahir Dar Institute of Technology, Bahir Dar University in partial fulfillment of the requirements for the degree of MSc in the Computer Science in the Faculty of Computing

Advisor Name: Tesfa Tegenge (Phd)

Co-Advisor Name: Arch Singer Getachew Birhanu (MA)

Bahir Dar, Ethiopia

February 25, 2019

DECLARATION

I, the undersigned, declare that the thesis comprises my own work. In compliance with internationally accepted practices, I have acknowledged and refereed all materials used in this work. I understand that non-adherence to the principles of academic honesty and integrity, misrepresentation/ fabrication of any idea/data/fact/source will constitute sufficient ground for disciplinary action by the University and can also evoke penal action from the sources which have not been properly cited or acknowledged.

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To all my best family

ACKNOWLEDGEMENT Before all, I approach my greatest praise and honor to the God of Heaven and His Mother and My Mother of perpetual help Saint Merry for blessing me in my life and providing me what is more important for me rather than what I want to be and making me the impossible seems, possible.

I would like to also label my special gratitude goes to my knowledge father and advisor Tesfa Tegegne(Phd) for his helpful comments, guidance ,suggestions and supervision starting from the instant of the problem devising even after that coordinating my work by following my weekly portioned tasks.

I would like to thank to the community of Bahir Dar Saint Yared Bagana Training Giving Center, Gonder, and Fnoteselam branches and Addis Ababa Sisay Bagana specially Arch singer Getachew Birhanu, Instructor Solomon, and Sisay Demissie for helping me in consulting and collecting data.

My special gratitude, honor and thank goes to my knowledge father as well as class advisor Dr.Gebeyoh Belay for his unforgettable memory and ever consultancy encouragement throughout my PG class and thesis work.

Last but not least i like to approach my heartedly thank to my best friends and families for their ideas and right help during the work.

Wendimu Baye

ABSTRACT

Song is a real occasion which is living with human life. Due to this, human had been using different accompanying instrument and discovering abstracts or features of song/music for better expression of their emotion (happiness, repentance and sadness). Even if, features of song /music have been set and standardized literally, but identifying in computerized way is somehow a challenging task. Among features of song/music scale is one sub-feature which play an important role in uniquely identifying melodies, since its properties are reflected to the melodic essence. The extraction and understanding of music scales are also essential in information analysis, retrieval and composition of music. In addition religious institutions like orthodoxy Christian, Muslim and including other institutions are very sensitive to music scales. Consequently, classic algorithms for identifying pentatonic scales have been designed based on the most popular scales; major and minor scales. In this research, we designed a model for identifying well-known Ethiopian traditional lyre Bagana song scales, such as Selamta, Wanen, Chernet and Bati Major. To identify the scales primarily we have compare three sample pitch detection approaches. Approaches like time, frequency and hybrid domain such as PYIN, ACF, CCF, SHS, and CAF. When we apply the detector algorithm on 713 melodies which were prepared for pitch detection we got the accuracy 98.4, 74%, 76%, 68% and 72% respectively. In this study, rule base approach is used by using PYIN, we extracted pitches frequency and it is converted to pitch note. Based on pitch occurrence ranking is performed. When extra pitch is not occurred normal identification were going to be performed, otherwise, by setting threshold value identification is done. Finally, the proposed model is tested on the data collected about 1357 of different audio category from concerned Bagana training institutes. The experimental result is shown (accuracy for single and double strumming 94.7% and 81.65% respectively).

Keywords: - pentatonic-scales, pitch set order, scale identification, range, major and minor key

TABLE OF CONTENTS

DECLARATION ...... i ACKNOWLEDGEMENT ...... v ABBREVIATIONS ...... x LIST OF FIGURES ...... xi LIST OF TABLES ...... xiv CHAPTER ONE ...... 1 INTRODUCTION...... 1 1.1. Background ...... 1 1.2. Motivation ...... 3 1.2.1. Historical Motivation ...... 3 1.2.2. Technological Motivation ...... 4 1.3. Statement of the problem ...... 5 1.4. Objective ...... 7 1.4.1. General Objective ...... 7 1.4.2. Specific Objective ...... 7 1.5. Scope and Limitation of the study ...... 8 1.6. Significance of the study ...... 8 1.7. Beneficiary of this research...... 9 1.8. Methodology of the study ...... 9 1.8.1. Data Collection ...... 9 1.8.2. Implementation Tools ...... 9 1.9. Research Design...... 10 1.10. Structure of the thesis ...... 11 CHAPTER – TWO ...... 12 Literature Review ...... 12 2.1. Overview ...... 12 2.2. Pitch detection approaches ...... 15 2.2.1. Pitch extraction Tools ...... 20 2.3. The Ethiopian Lyre Bagana ...... 22 2.3.1. Bagana physical architecture and Strumming ...... 22

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2.3.2. Ethiopian Bagana Song architecture ...... 22 2.4. Research works in Bagana ...... 23 2.4.1. Anti-pattern Discovery in Ethiopian Bagana Songs ...... 23 2.4.2. Contrast Pattern Mining of Ethiopian Bagana Songs ...... 23 2.4.3. Generating Structured Music For Bagana Using Quality ...... 24 Metrics Based On Markov Model ...... 24 2.5. Related Work ...... 24 2.5.1. Music Scale recognition via deterministic walk in a graph ...... 24 Chapter Three ...... 32 Methodology ...... 32 3.1. Overview of Music Scale Identification ...... 32 3.2. Design Methodology ...... 33 3.3. Architecture of Instrumental Song of Saint Yaredic Chant Derived Automatic Scale Identification ...... 33 3.3.1. Pitch Extraction ...... 47 3.3.2. Approach ...... 48 Chapter Four ...... 58 Experiment and Result ...... 58 4.1. Introduction ...... 58 4.2. Data Preparation...... 58 4.3. Testing Data ...... 60 4.4. Pitch Detection Comparison Result ...... 60 4.5. Prototype Development Using Integration of Microsoft Office Excel ...... 61 And Visual Studio C# ...... 61 4.6. Prototype Interface and Operations ...... 62 4.6.1. Loading Audio and Extraction of Pitch Frequency ...... 63 4.6.2. Perform Computation ...... 64 4.6.3. Scale Identification Interface ...... 65 4.7. Results ...... 67 4.8. Result Discussion ...... 72 Chapter Five ...... 74 Conclusion and Recommendation ...... 74

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5.1. Conclusion ...... 74 4.9. Contribution ...... 75 4.10. Recommendation ...... 76 REFERENCE ...... 78 Appendix -1: Document related to some facts about Bagana ...... 82 Appendix-2 Saint Yaredic Chant Derived Pentatonic Scales ...... 84 Appendix 4: data collection ...... 88 Appendix 5: Sample Implementation Source Code ...... 90

ABBREVIATIONS ACF Auto-Correlation Function

AMDF Average Magnitude Difference Function

ANN Artificial Neural Network

BMR Bagana Melody Range

CAF Cepstrum based Autocorrelation Function

CCF Cross-Correlation Function

DARM Data Reduction Method

ECKF Extended Complex Kalman Filter

EOT Ethiopian Orthodox Tewahido

GUI Graphical User Interface

IEEE International Electrical and Electronic Engineering

LPC Linear Predictive Coding

KNN K-Nearest Neural Network

MIDI Musical Instrument Digital Interface

SHS Sub-Harmonic Summation

SIFT Simplified Inverse Filtering Technique

SVM Support Vector Machine

VMR Vocal Melody Range

LIST OF FIGURES

Figure 2-1: Overview of the musical features extracted with mirtoolbox.(Lartillot &

Toiviainen, 2007)...... 14

Figure 2-2: MFCC for timbre analysis (Lartillot & Toiviainen, 2007) ...... 14

Figure 2-3: Chromogram for tonality (Lartillot & Toiviainen, 2007)...... 15

Figure 2-4: Fluctuation for rhythm (Lartillot & Toiviainen, 2007) ...... 15

Figure 2-5: plot of (a) ground truth (blue), (b) ECKF pitch detector (red), and (c) YIN pitch Estimator (green)(Das, Iii, & Chafe, 2017)...... 20

Figure 2-6: relationship between structure s, t, and tm(Zhao & Andres, 2016) ...... 27

Figure 2-7: diatonic and pentatonic scale ...... 29

Figure 2-8: pentatonic scale ...... 30

Figure 3-1: the architecture of Begena song pentatonic scale Identification ...... 34

Figure 3-2: audio Bagana song of archi singer‟s alemu aga “manyimeramer” ...... 42

Figure 3-3: pitch comparison diagram ...... 43

Figure 3-4: pitch extracted using CAF...... 44

Figure 3-5: Extracting pitches and displaying with notepad file format ...... 45

Figure 3-6: Praat ACF defaults setting ...... 46

Figure 3-7: Tony software tool interfaces with audio attributes ...... 47

Figure 3-8: when A man Strumming Bagana, pitches are emanate and go out to surrounding ...... 48

Figure 3-9: pitch counting procedure ...... 49

Figure 3-10: give rank for each pitch based on their occurrence ...... 50

Figure 3-11: top 5 pitch selection ...... 51

Figure 3-12: rescanning pitches either they are pentatonic category or not ...... 53

Figure 3-13: song scale identification Model ...... 56

Figure 4-1: Microsoft Visual Studio Professional 2015 ...... 62

Figure 4-2: Prototype Implementation Design ...... 63

Figure 4-3: Loading Audio and extracting pitch Frequency ...... 64

Figure 4-4: procedure and algorithm computation ...... 65

Figure 4-5: Scale Identification Interface ...... 66

Figure 4-6: graphical representations for correct and incorrect scales of melodies ...... 69

Figure 0-1: Emperor Haileslassie Plucking Bagana ...... 82

Figure 0-2: alemu aga playing Ethiopian lyre Bagana song concert to white people...... 82

Figure 0-3: (a) Harp in Christ Church Jerusalem Israel (b) Hiding place played on the harp ...... 83

Figure 0-4: (a) Ethiopian megabesibhat Bagana strumming using Fingers method of .... 83

Figure 0-5: Bagana body parts...... 83

Figure 0-6: selamta scale arrangement on staff ...... 84

Figure 0-7: Finger position on Bagana strings ...... 84

Figure 0-8: Chernet scale arrangement on staff ...... 84

Figure 0-9: Wanen scale arrangement on staff ...... 85

Figure 0-10: (a) Selamta scale, (b) Wanen scale and (c) Chernet scale ...... 85

Figure 0-11: letter of support ...... 88

Figure 0-12: interviews with arch singer Getachew Birhanu ...... 89

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Figure 0-13: interview Questions with concerning body ...... 89

Figure 0-1: thesis implementation model code part-1 ...... 90

Figure 0-2: thesis implementation model code part-2 ...... 91

Figure 0-3: Loading Data and perform computations part-1 ...... 92

Figure 0-4: Loading Data and perform computations part-2 ...... 93

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LIST OF TABLES

Table 2.1: Error Statistics for Figure2-5 ...... 20

Table 2.2: some selected code scales (Zhao & Andres, 2016) ...... 26

Table 2.3: some variable value used by scales encoding algorithm (Zhao & Andres, 2016)

...... 26

Table 2.4: validity checking of Ethiopian Pentatonic scale with via deterministic graph 31

Table 3.1: Source of data ...... 36

Table 4.1: prepared training pitch dataset format ...... 59

Table 4.2: correct and incorrect scale identification table ...... 68

Table 4.3: correct and incorrect scale identification table ...... 68

Table 4.4: pitch occurrence and average wasting time for pitches ...... 69

Table 4.5: Accuracy result summary ...... 71

Table 0.1: Sample taken from arch singer alemu aga Bagana song (tew simagn agere song) with in 51 second and 060657596 microseconds and pitch occurrence...... 86

Table 0.2: Table 1.2: ranking pitches based on occurrence ...... 86

Table 0.3: selecting top 5 first rate pitches ...... 87

Table 0.4: revisit arrangement ...... 87

Table 0.5: measuring distance between well-adjusted pitches ...... 88

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CHAPTER ONE

INTRODUCTION

1.1. Background

Song is everywhere we go, we can listen it from radio, television, movies/films, cinema, Cafeteria, coffee shop and even on our cellphones. As common listeners, we are not expected to have a deep understanding of the science of song/music to appreciate it. However, those who are assumed singers/musicians, in another way often devote years in learning the details of the art (G.Wyvill, 2003). They are about digging profound into the theory, exercise and rehearse routinely to perfect their skill. Eventually, they are expected to capable to train their ears to listens in sound that a typical person could just be able to perceive.

Over the previous few decades with the introduction of computers, numerous supporting tools have been invented for singers/musician in different ways. Musical instrument tuner, recording software, audio recording, and music composer (MIDI) are readily available(G.Wyvill, 2003). One area that is still being keenly explored is music information analysis. Computer software package have been scripted to stab to identify the pitch, tonality, scale recognition and duration of exact notes in audio files for the purpose of automatic music transcription(Ryynänen & Klapuri, 2005), (Zhao & Andres, 2016) and for other purpose are required. Many solutions have been proposed. However, still it requires a better methods and techniques.

When we see its scientific architecture of song/music, basic building blocks of song/music that are chants, the basic building block of chant are scales and pitches. Pitches are derivation of scales. Pitches are the smallest unit of any song/musical instruments derived from natural sound. Chant is the arrangement of sound/music notes to write, and read music/song alike alphabets of human language(Sisay, 2009).

As the speeches that we heard are the arrangement of different phones or sounds and they have their own shape and sound, similarly, chants have different arrangement of chant sound. The base for chant is sound, without sound it is impossible to get chant. All the sound that we heard couldn‟t the result of the chant sound. Sound that we heard divided into two: - (a) Noise: - uncomfortable to ear, it doesn‟t create sense rather disturbing, (b) Musical sound: - comfortable to ear, it can buy attention. There are 7 different chant sounds (tones) from these chant sounds there are additional 5 sounds each by adding half we can get another sounds. In total we can have 12 different semitones (chromatic). To add and subtract sounds by half there are symbols like #  represent adding tone by half, b this also subtract tone by half(Schmidt-jones, 2007), (Boonmatham, & Pongpinigpinyo, 2013).

At the observation of piano roll or organ, the distance between chant sounds is measured by tone. There is semi (half) tone sound in natural scale such as in between 3rd and 4th, in between 7th and 1st. Scale help us to create chant which is a collection of sound. Therefore, there are different types of scale around the world some of the dominant are Chromatic ,Diatonic, and Pentatonic Scales(Boonmatham et al., 2013).

The acoustical characteristics of the scale of song are described based on melody and contributed to song category. Song scales have varies contributions in analysis of music composition theory, music genre, Music Information Retrieval, evaluation of happiness, sadness (Series, 2015), (Attakitmongcol k., chinvetkitvanit R., 2004), (Sternberg et al., 2016). Identification of scale of melody is necessary in many large musical works as well as some times in some scale sensitive chant or song of religious institution (like EOT, Muslim). However, still it is a tiresome work. For that reason, in literatures it has been proposed different methods. The 1st algorithm for sensing the tonality to be implemented in computer was the longuet-Higgins and Steedman‟s algorithm(Steedman, 1971). This algorithm encouraged to the development of Krumhansl Schmuckler‟s algorithm(Rudolf et al., 2014). Onwards other algorithms have developed(Carley Tanoue, 2004), (Levine, 2015). Based on the major and minor scales; some approaches require prior information for detecting the tonality. However, according to different study enumeration of scales is

2 great(David L, 2013). Based on this with no prior knowledge of the profile; scale recognition has been done with chromatic structure interval 1,2 and 3 semitones (Zhao & Andres, 2016).

So, this study focuses on the pentatonic scale of Ethiopia lyre Bagana Song. This scale has 5 different chant sounds. Ethiopia is one of the countries that use pentatonic macro- tonal scale. By considering pentatonic scale, we have been identified the pentatonic scale of the Bagana related spiritual song, weather it is Selamta(ambassal major), wanen(Tizita Major), Chernet(Anchihoye Lene), Bati Major or other pentatonic scale(Sisay, 2009)(Abate, 2009). A comparison is made between three domain approaches (time, frequency and hybrid domain approaches). By using PYIN pitch detector as belongs to time domain, we extracted pitches from Bagana song, then evaluate and identify the scale of the song by the new proposed model.

1.2. Motivation

The main motivating factor for this study is: Historical and Technological.

1.2.1. Historical Motivation It is known that Ethiopia is a country of endowed with human knowledge, wisdom, expert and natural art in the world of song at the era of unknown song notations, existed with its own symbolical representation of melody/chant (Yelibenwork Ayele, 2008)(Dr.Jacopo Gnisci, 2016), (Ethiopian Tourism, 2012). As it is reported in history and literatures , song has been existed for at least 55,000 years and the first music have been invented in Africa and then grew to become a fundamental component of human life(V.Bohlman, 2013)(Krause, 2012). However, the invention of keeping the structure of song with symbolical depiction was in 6th century by the honored Ethiopian chant expert St.Yared. modal world song note creation had been emerged most in medieval or renaissance age 14, 15, 16 century and onwards in England by Queen Elizabeth and known music expert/composer like Hilegard von Bingen, Moniot d'Arras, Adam De la Halle, Guillaume de Machaut, perotin,Tylman Susato and the like(Jim Paterson, 1999). From this what we recognize; Ethiopia has been the birth place of song/music notations of melodies with 8 century differences from the world standard music notations. When

3 we raise St.Yared at the back we have to remember EOT church religious institution because major his works are primarily designed to this religious institution. This religious institution perform daily, weekly, monthly, and yearly of any internal and external chant(zema) services are based on St.Yaredic chant scales(geez, ezil, araray). As EOT religious fathers agreed, most world songs are derived from Yaredic chant. The song instrument (Bagana, washint, masinko, etc) who they use should tune in Yaredic derived chant scale. Out of Yaredic chant scale any of the vocal song as well as instrumental song has no validity for this religious institution(Tsehay Birhanu, 2018). For this reason, Our study focused on St,Yaredic chant derived pentatonic scale identification(selamta, wanen, Chernet and Bati major) on specific spiritual instrument which is Bagana(chordophone instrument) (orth, 2018) , (Schmidt-jones, 2012) so as to specific the scope.

1.2.2. Technological Motivation

The area is incorporating composition of varies fields of study, core sciences such as Mathematics, Physics, Statistics, Digital Signal Processing, Electrical & electronics engineering, and Computer Science as a base for development and advancement of multimedia technology. So, on root help me to ask and get response for the questions (what are sound signals? How these sound signals derived? How to shape and administer sound signals in order to use for different application area? based on field of study electrical & electronics engineering and working together with mathematics, physics ,statistics and computer science) to see these core sciences before to deepen my major task on computer science (devising model/algorithm).

Next, we were fascinated how the fundamental elements of music (rhythm, pitch, melody, texture, tone-color, form and etc) evolved from fundamental elements of sounds (frequency, amplitude, waveform, and duration).

Finally we were surprised why not still the model and approach has not been devised to identify the Ethiopian pentatonic scale. World widely music scale recognition has been designed. However, Ethiopian Pentatonic scale such as, Bati major has not been recognized.

1.3. Statement of the problem

Earlier time in the area of multimedia technology especially in music technology has been brought dramatic change in solving many traditional musical instruments bringing into digital format. with the help MIDI software tools for copying instrumental sounds, music edition, production and some other related tasks such as fl-Studio, Tuner, Able Tone, Sonic Visualizer and some other.

However, now a day‟s events are rapidly changed because of deep interest of human being towards song/music. The major concern of world music industries, institutions and music talent show giving centers are recently based on the high level and low level music features for the better satisfaction of their followers and patrons. So, music researchers around the world are now attentively investing their efforts in studying each feature of music. Even if considerable works are available. But, still it is challenging area (for more take a look at chapter 2 literature review part).

Several music researches have been carried out in Ethiopia; however, to the best of our knowledge there is no technology based music research so far. Chants, Songs, (religious and non-religious) singers are currently going with a traditional way of conducting music. Most of Ethiopian music professionals does not used technological methods devised to know the features and sub features of music and the methods they are using to know scale, rhythm, timbre, melody, pitches, tone and harmonic of music; instead of listening and discussing the features.

Scale is one sub features of music. So, as much as possible song professional and experts they set rules for scale, tone, major/minor key and range of song manually. However, sometimes bias has been observed among music students, teachers and professionals. This is because beyond being manually there is some natural factor that can affect the way of recognition or identification of scales due to human ear pitch detection problems and human psychological factors(Susini & Lemaitre, 2011) & (Kujkarn, 2001).

As stated above and previous lessons, world music scale recognition has been developed. However, some indigenous and foreign pentatonic scales are incapable to be recognized and identified by priori developed model. So, we devised a model to identify yaredic

5 derived specific chant scales based on Ethiopian Lyre Bagana song. Ethiopian Orthodox Tewahido Religious Institution is one of zema sensitive religious institution. Currently, many singers of the clerical Orthodox Tewahido Church are exits; among them we observed some Bagana songs specially those which have not been verified by mahibere kidusan multimedia and organization association has out of yaredic chant. This is due to properly unrecognizing of tonality, keys behavior, pitch frequency and timing, even also what keys they are involved in each scale of the song. The impact of this leads to deteriorating, degrading and perplexing the followers as well as unique scale heritage. In addition this it lead also lack of listener and follower for the singer. Therefore, the following detailed problems are noticed before we formulate the research question:-  The major or minor keys (pitch); perceptually the simplest means to know the difference between major and minor keys is based on the emotion their sounds educe. Major keys in nature they do have a bright, happy and cheerful melody; whereas minor keys sounds more melancholy, miserable, depressed and sad. Scientifically, major and minor keys can be identified through their tonic note which mean their starting or root note, and it is also possible to know based on their major or minor scale of the melody(Musical UTeam, 2016). However, exact major and minor key with range based is a challenging task.  Scale; a scale is made of collection of pitches which mean with a tonic distance between pitches, and scales are the basic building blocks of melody, melody is a collection of one or more scales. So, scale identification of melody is important in music world. However, still a challenging task(Zhao & Andres, 2016).  Range is one characteristic of melody and scale, which describes the distance between the lowest and the highest tones. So, Begena singers are sometimes complicated with the arrangement of being in a low, medium, or high range, meaning that the notes focus on those scale pitches(Feng, 2012).  Sometimes professional of Begena singer unclear with some features of the song which is expressed in electronic format(they have tried to express literally what difference has in between in each pitch of the scale and scale of the chant. however what frequency each pitch has is difficult to measure for them).

This study intends to address the following research questions:-  How do users properly tune or sense Bagana strings (membrane pillar)? And how much tone is remaining or requires to bringing and adjusting the required scale?  How do we automatically identify the major and minor key of singer?  What are the mechanisms to recognize the ranges and of the singers with the help of technology?  Is there any technology supported methods to correctly identify the scale of the required musical instrument of the singer?

1.4. Objective

1.4.1. General Objective

The general objective of this research work is to design and develop the instrumental song of Saint Yaredic chant derived Automatic Pentatonic Scale Identification using newly proposed model on Ethiopian Lyre Bagana.

1.4.2. Specific Objective

To fulfill the general objective these specific objectives must be achieved:-  To arrange the pitch data based on key order. Since pitch sequence are located at the same or different position  To determine the major or minor key. since the major/minor key determines where the range it goes and stops  To locate the octave of the melody/song  To measure the distance from major/minor key to the remaining key in order to know the tone  To develop a prototype to identify automatically the scale of Begena song

1.5. Scope and Limitation of the study

The study is bounded only with Instrumental song of saint yaredic chant derived Automatic Pentatonic scale playing modes Selamta, Wanen (Tizita Major), Chernet (anchihoye lene) and Bati Major Identification.  Very noising musical instruments (speaker or montarbo) are not incorporated. For example: - Big montarbo because they may be mask macro pitches sound totally.  Incorporated musical instrument(classical instruments) primarily Ethiopian lyre Bagana  Scale of the vocal chant incorporated. However, scale of the vocal is must be below the range of Begena.  It is octave based. However, Intonation is excluded.  Reversed song scale of strumming is excluded.

1.6. Significance of the study

The importance of this study is:

 Evaluate Bagana singer/musician of the Ethiopian Orthodoxy religious institution to properly identify the scale of the song, by properly studying timing, frequency and range of the Begena song.

 Help for the followers of orthodox tewahido Christian as well as music professionals and experts in identifying those basic features (major/minor key,tone, range and scale).

 Since Ethiopians were the starter and the basement of modern music technology and these yaredic derived chants are our national heritage. They need study, research, well digitized and enable then to resume this generation and transferred to the next/coming generation.

 So, since every song/singers sound is unique and dynamic in nature due to the major/minor keys, tones, colors and the range of the singers used are different. Consequently, after this study would have completed and deployed to

application song/singer‟s major key, range will be known and the scale will be identified.

 Begena will be tuning properly with the required frequency without hesitation. It is possible to strumming Begena in group by tuning all in the same range

1.7. Beneficiary of this research

 Ethiopian Begena trainers and educators  Solo singers of any Ethiopian typical chordophone instrument pentatonic scale players.  Ethiopian music pentatonic scale researchers

1.8. Methodology of the study

To achieve the objectives, Experimental research methodology is employed. The study uses different research methods or techniques.

1.8.1. Data Collection

Data was collected for testing from concerning body like Bahir Dar St.Yared Bagana Training Institute, Gonder St.Yared Bagana Training Institute, Dangila St.Dawit Begena Institute, Addis Ababa (Sisay Begena and traditional instrument training Institute) and YouTube.

1.8.2. Implementation Tools

 Leapic Audio Cutter: - this software tool helps us to censor the required song/music by specifying files with time.  Audacity: - help us to reduce noises, in addition to help us to mask background music arrangement and allow foreground singer and vice versa.  Total audio convertor: - convert the file format of the recorded file format into the tool accepted format. E.g.:- .mp4 to .wav  Stepvoice recorder:- records the song/music sound  Excel :for formulating and preparing dataset

 Visual Studio C#/C++: for analyzing and implementing the research designs.  Praat and Tony: for pitch extraction

1.9. Research Design

A research design includes the structure of a study and the strategies for conducting that study (N, 1973). For the purpose of conducting this research, experimental research design is used. It is a way to carefully plan experiments in advance so that results are both objective and valid (Nayak & Singh, 2015).

The experimentation conducted in this study is separated into two experiments, with each experiment differing with regards to the selecting the best detector of the music pitch and scale identification.

The first experiment is conducted in choosing the best pitch detector using collected audio data. The second experiment is tested for identifying the scale of Begena. Furthermore, this experiment investigates the effect of the output. The results of the experiments are analyzed individually at the end of each experiment and an overall analysis is done to summarize the observations.

Literature review To properly understand the Area, the specific problem, and the proper approaches of the problem solving process, relevant literatures reviews related to song scale Identification that are considered to be relevant for this work are investigated. Related literatures from different sources (Books, Journals, Internet, etc.) are reviewed to understand scale Identification, its development tools, techniques, procedures and methodologies.

Preparation of the data Dataset of the classical/melody pitches are prepared based on the worldwide standard using mathematical formula(Hilbish, 2012). Different pitch detection and preprocessing techniques are applied for processing melody; time domain and frequency domain approaches. For preprocessing we used for praat tool or Tony(Mauch et al., 2015)(Mauch et al., 2015).

1.10. Structure of the thesis

The remainder of the thesis is organized as follows: chapter two covers literature review, overview of pentatonic macro tonal scales, approaches to Melody Scale Identification, evaluation song scale identification methods, and related works. The methodology and the techniques used in this study is presented in chapter three. It discusses data preparation for the experiment and models that are implemented. Chapter four discusses the experiment and the results. Finally, chapter five presents conclusions and recommendations.

CHAPTER – TWO

Literature Review

For the sake of locating viewpoint of the research, to rationalize, to make sure either the research has not been just a replicated study;  To know in what way has been done the previous studies?  To locate what previous research highlight the gap?  To either the researchers enable to learn theory from previous study or not? And  What work additions, understanding and knowledge to the area, and locating the gap? All these main points are asked and gain response through when reviewing varies research literatures(Beile, 2005). So, this Chapter discusses fundamental concepts of music information and music information identification, with specific and impartial of accessing fundamentals of music like: - pitches, range of songs, major and minor scale of songs. With general objective focused on pentatonic scale identification and ideas associated with major Ethiopian yaredic derived macro-tonal scales; like Selamta (Ambassel Major), wanen (Tizita Major), chernet (Anchihoye Lene Major), and Bati Major using religious musical instrument Bagana. Scale identification methods are going to be presented in order to grasp clear overview of the topic. The aim of this study is to design and develop instrumental song of saint yaredic chant derived automatic pentatonic scale identification in case of Bagana using new proposed identification algorithm.

2.1. Overview

Music is one of the application areas of multimedia technology and digital signal processing, and together they bring audio. Multimedia is about how information represented through information processing elements (such as computer, television, and etc) information like sounds (such as music, speech, noise) in the form of audio, script in the form of text, physical picture in the form of image/ drawing, real events and action related to movement in the form of video, the quality of things such as text, picture, video

12 justify via in the form of graphics, and etc(Vaughan, 2006). Whereas, Digital signal processing also a broad wide area and most powerful technology field of study in keeping and silhouetting science and engineering. by the use of digital processing element such as computers, performing a wide variety of signal processing operations(such as in the field of high fidelity music reproduction, sonar systems, communications, medical imaging, radar system, oil and fuel prospecting to just name a few). Those varieties of operations are performed via a technique of digitization. Digitization is about converting analog to digital or continuous to discrete value. So the signals that are going to be processed are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space and frequency and with discrete value digitize data with sampling techniques(Smith, 1999).

Our focus area is on fidelity of music reproduction with sub specific Area of music information analysis. When we say music is described of different perceptual components rhythm, dynamic, melody, harmony, tone color, texture, form and other with micro components like scale, range, major key and etc. Having these basic components help us to evaluate the performance of song. A lot of studies have been tried through time and even improving now days also in relation to these basic components.

According to study in the Area of Music Information Retrieval/Analysis (MIR/A) music which is in the form of audio/Audio Signal waveform has different basic digital features like zero crossing rate, root mean square energy, envelope, spectrum, filter-bank and other micro features spectral flux, mel-scale spectrum, chromagram, centroid, kurtosis, skewness, flatness, irregularity, fluctuation, and etc for more see detail bellow Figure 2.0.

Figure 0-1: Overview of the musical features extracted with mirtoolbox.(Lartillot & Toiviainen, 2007). Using these basic and sub basic components of digital features of music it is possible to make timbre analysis, tonality analysis, rhythm analysis, energy analysis and etc. For example: - one common way of describing timbre analysis is based on MFCC look bellow (Figure 2.1). For tonality analysis schmuckler and krumhansle have proposed methods for extracting tonality of musical piece look bellow (Figure 2.2). For rhythmic analysis a common ways is based on auditory modeling(Irtoo & Lartillot, 2017) (Figure 2.3).

Figure 0-2: MFCC for timbre analysis (Lartillot & Toiviainen, 2007)

Figure 0-3: Chromogram for tonality (Lartillot & Toiviainen, 2007).

Figure 0-4: Fluctuation for rhythm (Lartillot & Toiviainen, 2007)

These audio features can be implemented in different research area such as: Audio effects feature extraction, Statistical synthesis, Feature based synthesis, Music audio key detection, Music audio similarity and classification submissions, Music genre classification(Boonmatham et al., 2013), Melody note transcriptions(Mauch et al., 2015), Automatic music classification and importance of instrument identification(Cory Mckay, 2005), etc. So, there are a well know tools which are used throughout music audio content analysis and retrieval based on coverage, effort, lag time, presentation(David Moffat , David Ronan, 2015).

2.2. Pitch detection approaches

Pitches are the very fundamental things in Music Information Retrieval/Analysis, speech recognition and in general for sound related works(Smith, 1999)(Kubrick & Clarke, 2007). Pitch is the perceptual attributes of sound which allows the ordering of sounds on frequency related scale extending from low to high (Milano, 2012). The idea of pitch was primary existed in Digital Signal Processing with the core science physics, mathematics and electronics and communication engineering. Later, according to its broaden usage

15 other field of study consumed widely like Natural Language Processing (in a research topics like speech recognition, synthesis, identification, and translation), and Multimedia (sound related works like Music Information Analysis, Music Information Retrieval, Music Production and the like) Area‟s.

Primarily pitches in Digital Signal Processing have three detection methods; Time Domain, Frequency Domain and spatial domain. When we say time domain; whenever a signal that uses time as independent variable which is when parameter lays on horizontal axis. Whereas, frequency domain as well whenever a signal uses frequency as frequency domain (horizontal axis). Similarly, signals that use distance as a parameter we call it spatial domain(Smith, 1999).

Since pitch is a purely a human perceptual feature of music, i.e. it signifies the way that music sounds once it has been converted by our sensory organs into neuro-electrical signals and administered by a section of our brain, it is a very difficult problem to try and pinpoint pitch using a computer(Müller et al., 2011). Putting it another way, the aim of pitch detection is to process a signal in whatever way most accurately determines what it would sound like if that signal were to be physically produced and then experienced by a human.

Though each note is composed of many different frequencies, the perceived pitch of the note commonly equated with the fundamental frequency of the note, since this is usually the bases from which the other harmonic frequencies are derived. Given this, many pitch detection methods focus on identifying the fundamental frequency or frequencies of a sample(Salamon & Emilia, 2010). This is not an accurate model of pitch, since real instruments produce complex sound waves with many overlapping frequencies, especially in the upper ranges. However, in most cases this is a necessary simplification that makes the problem of pitch detection much more manageable. Given this, the goal of a large number of pitch detection algorithms is to make this simplification from a rich waveform with many harmonic components to one fundamental sinusoid with a set frequency as clean and accurate as possible(Müller et al., 2011). Obviously, the more harmonics contained in the waveform, the more difficult this becomes. Due to these typical reasons, three different pitch detector approaches have been developed.

Which pitch detector domain approach is preferable?

The above listed pitch detection approaches have their own application domain according to their usage. Most frequency domain approaches are preferable in speech related tasks; like speech recognition, speech synthesis, and speech translation and related works(Kubrick & Clarke, 2007). Since frequency domain approaches follow a spectrum of spectrums they are sensitive to environmental noises (pity or trivial noises) to perfectly detect music pitch they require music studio (silent room). Whenever many harmonics of music sounds (verities of instrument sounds) and speech sounds frequency domain approaches are preferable. Since its derivation is based on the Fast Fourier Transform (FFT) or inverse Fast Fourier Transform (e.g:- Cepstrum pitch detector) it doesn‟t miss any harmonic sounds (Salamon, 2014) (Roche, 2012). This encapsulates the basic idea, but there are many algorithms building off of this idea with refinement and tuning either aimed at cleaning the signal before processing it, or transforming it more accurately into frequency values (Pauws 2004, Zhu 2006, Salamon 2014). However, time domain approaches are better suite in music pitch detection, music tone and music scale. Since time domains approaches are beyond they are simple they are adaptive to small noises(Levine, 2015) (McLoad, 2014) because their derivation is designed based on autocorrelation(the peak of the original function). So, to detect pitches, to measure tone and to know scale of music time domain approaches are more preferable specially for monophony instruments or solo(single instrumental player).

How pitches are detected or extracted from music and speech? Varieties of algorithms were proposed since in 20th century up to now, IEEE fellow and member lawrence r., michael j., and aaron e. (1976) respectively; they had been conducted a study on comparisons of performance analysis of different pitch detector algorithms. Primarily they had been prepare speech utterance spoken by some male, female, child, telephone conversion, close-talking microphone and wideband recording based on these they had been applied using time , frequency and hybrid pitch detection algorithms. 1. Autocorrelation Method (AUTOC), 2. Cepstral Method (CEP), 3. Simplified Inverse Filtering Technique (SIFT), 4. Parallel Processing Time Domain (PPROC), 5. Data

Reduction Method (DARD), 6. Spectral Flattening Linear Predictive Coding (LPC), 7. Average Magnitude Difference Function (AMDF). From these; number 1, 4, 5, 7 are time domain, and number 2 is frequency domain and number 3, and 6 are hybrid one. So, according to analysis from frequency domain cepstral domain has low error rate whereas from time domain autocorrelation method and average magnitude difference function are the preferable (Rabiner, Cheng, Member, & Rosenberg, 1976).

Beyond these algorithms there are another popular pitch detector algorithm when they are compare with other remaining pitch detector algorithms based on three speech database DB1, DB2, and DB5(Cheveigne, 2002). A comparison has made in between these databases based on Gross error rates. Gross error rate measured using alternative ground truth. DB1: manually checked estimates derived from the laryngograph signal using the Tempo method of Kawahara et al. (1999b). DB2 and DB5; estimates derived independently by the authors of those database.

Figure: 2.4 Gross error rates for several F0 estimation algorithms over 4 databases(Cheveigne, 2002). From the above table kinds of estimators there are; however YIN F0 estimator is the prominent and preferable one. YIN is worked based on the well-known autocorrelation

18 method with a number of amendments to prevent errors, based on a test conducted on different database (DB1, DB2, DB3 and DB4) speech audio files; it brings appreciable Gross error rate.

However, now a day better and latest algorithms for pitch detection are existed that goes with the state of art which are PYIN and ECKF. PYIN was the modification of well- known fundamental estimator of YIN algorithm. Traditional PIN is a simple wise effective algorithm for frame based monophonic fundamental frequency estimation and brings to be one of the preferable methods in this area. So as to remove fraction of errors, outputs of frequency estimators are usually post-processed resulting in a smoother pitch track. As a result of output methods YIN makes precisely an estimate per frame; that is the way why YIN incapable post-processing falls back on alternative interpretations of the signal. To eliminate the problem that occurred in YIN, the advanced of YIN which is PYIN has existed to fill the gap on robustness of octave errors and voicing detection by following probabilistic threshold distributions as observation of Hidden Markov Model.

Another pitch detector is real-time pitch tracker Extended Complex Kalman Filter (ECKF); this is the safeness of this pitch follower that it operates on step by step basis, it is unlike other block based algorithms that are most commonly used in pitch estimation. If we take a look at their error statistics let‟s have observe bellow figure 2.1.

Figure 0-5: plot of (a) ground truth (blue), (b) ECKF pitch detector (red), and (c) YIN pitch Estimator (green)(Das, Iii, & Chafe, 2017).

Table 0.1: Error Statistics for Figure2-5

So, based on these above observable analysis YIN is appropriate for monophony sound music music pitch estimation(Matthias & Simon, 2014).

2.2.1. Pitch extraction Tools

Pitches are the fundamental things where widely used in many sound related applications, music related systems, speech recognition, speech synthesis and translation systems. Through time some digital signal processing approaches and many methods or algorithms has been proposed. These methods and algorithms have devised in the forms of tools now

20 days. Tools like: - PRAAT, RAPT, SRH, Tony, SSH, and STRAIGHT(Babacan, Drugman, D ‟alessandro, Henrich, & Dutoit, 2013).

PRAAT: - Normally widely used in speech research center, it is in the form of package (Boersma, 1993) offers two different domain approaches of pitch detector methods. The default technique in Praat is which is based on an accurate autocorrelation function. This approach was shown in (Boersma, 1993) to overtake the original autocorrelation based and the cepstrum based technique on speech recordings.

RAPT: - this tool released in the ESPS package, RAPT (Talkin, 1995) is a robust algorithm that uses a multi-rate approach.

SRH: - As it is clarified in (Drugman &Alwan, 2011), the Summary of Residual Harmonics (SRH) method is a pitch detector exploiting a spectral criterion on the harmonicity of the residual excitation signal. In (Drugman &Alwan, 2011), it was shown to have a performance comparable to the state-of-the-art on speech recordings in clean environments, but its use is of particular interest in adverse noisy environments. It is possible to use the tool found in the GLOAT package.

SSH: - This method is a different of SRH which works on the speech signal openly, instead of the residual excitation.

STRAIGHT: - STRAIGHT (Kawahara, Estill, & Fujimur, 2001) is a qualified speech synthesis, modification and analysis system based on a source filter model. There are two pitch extractors available in the package the more recently integrated one as published in (Kawahara, de Cheveigné, Banno, Takahashi , & Irino, 2005). This technique is based on both time domain and frequency domain approaches, and is intended to reduce perceptual disturbance due to errors in source information extraction.

TONY: - this software tool provides an accurate transcription of notes and pitches. It is an interactive tool which is based on PYIN(Mauch et al., 2015).

2.3. The Ethiopian Lyre Bagana

2.3.1. Bagana physical architecture and Strumming

Ethiopian Bagana has unique architecture, method of strumming and the physical body parts from which it is made up of in comparison with Israel harp of David (Israel Bagana), take a look appendix-1 Figure 0.2, and Figure 0.3.

Religiously according to Megabi sibhat alemu aga (well-known Bagana strummer) and Bagana instructor Sisay Demissie each physical body parts has represented appendix-1 Figure 0.4. Not only different by structure and method of strumming. However, they are different in music rhythmic, color, scale and some other music features.

2.3.2. Ethiopian Bagana Song architecture

(Scale architecture) In Ethiopia, Bagana training institutes have been conducted based on the following pentatonic scale structure. Before that, since finger code number and their position is a key determinant in identification appendix-1 Figure 0.7.

Selamta scale: - this typical scale replaces anchihoye lene or Ambassel major, it‟s is derived from selamta Bagana song appendix-2 Figure 0.6. So Bagana with selamta scale architecture 3rd 1st 2nd 5th 4th F C D A G  sound 1 2 3 4 5  finger 1 4 6 8 10 string

Wanen: - this scale replace tizita major, it‟s is derived from wanen Bagana song. We can get wanen scale from selamta scale by releasing half tone of the 1st finger position of selamta scale appendix-2 Figure 0.9. So Bagana with selamta scale architecture 3rd 1st 2nd 5th 4th E C D A G  sound

1 2 3 4 5  finger 1 4 6 8 10 string

Chernet: - this scale can be created from selamta by releasing half tone of the 3rd and 5th finger position appendix-2 Figure 0.8. So Bagana with Chernet scale architecture

3rd 1st 2nd 5th 4th F C Db A Gb  sound 1 2 3 4 5  finger 1 4 6 8 10 string Examples of Begena songs; see at appendix 2 Figure 0.10.

From the above Bagana songs all Bagana song scale has the same finger representations. However, they are differing each other by tonal architecture of each scale. So how to design a system to identify either selamta, wanen, chernet or bati major? It is a challenging task.

2.4. Research works in Bagana

Four typical research works has been conducted on Ethiopian lyre Bagana among them three of them are emotion, structure, contrast pattern, and anti-pattern mining.

2.4.1. Anti-pattern Discovery in Ethiopian Bagana Songs

A corpus of Ethiopian Lyre Large Bagana songs was developed and applies sequential pattern mining. An imperative feature of this collection is an exceptional availability of rare keynotes that have been used by a master bagana teacher in Ethiopia. The method is applied to ﬁnd antipatterns: patterns that are surprisingly rare in a corpus of bagana songs(Conklin & Stephanie, 2014).

2.4.2. Contrast Pattern Mining of Ethiopian Bagana Songs

Based on small dataset of 37 Bagana songs have been encoded in terms of finger number representation. On this paper the mining method that they used was based on two separate configurations; pattern to musicians and pattern to scale. The dataset contains

1906 events, and represents seven different musicians and songs played in two different scales (called tezeta and anchihoye lene).

By setting a significant threshold α= 0.001 minimal pattern were mined, in order to remove art-factual intra-opus repetition(Conklin, Neubarth, & Weisser, 2015).

2.4.3. Generating Structured Music for Bagana Using Quality

Metrics Based On Markov Model

A system has been designed to generate a Bagana Song from a traditional lyre Ethiopia based on first order hidden Markov model, under the size of many datasets it is often only possible to get rich consistent statistics for low order models yet those do not handle structure very well and their output is often precisely repetitive.

For this matter a method has been proposed that allows the enforcement of repetition and structure of music and then different ways in which low order Markov models could be resumed to build quality assessment metrics for an optimization algorithm. These were then realized in a variable neighbourhood search algorithm that generates Bagana song(Herremans, Weisser, Soerensen, & Conklin, 2015).

2.5. Related Work

2.5.1. Music Scale recognition via deterministic walk in a graph

Lots of work has been conducted on varies types basic and sub-basic elements of music such as rhythm, dynamics, melody, harmony, tone color, texture, timbre, form, tempo, beat, tempo, crescendo, forte, decrescendo, piano, pitch, chord, progression, consonant, tonality, key, range, monophonic, polyphonic, imitation, and etc(Goto & Muraoka, 1994)(Laroche & Ø, 2001)(Scheirer, 1998)(Foote & Uchihashi, 2001)(Patel & Gopi, 2015)(Rabiner et al., 1976).

More attention has not been given for identification of scale of music and even the range of music, the most studies before 2016 has been involved in music genre classification, pitch detection or extraction for music and speech recognition systems, tempo tracking, key pattern mining, and melody extraction from polyphony song/music and like.

However, scale identification of song/music is a very challenging task still; Even if, a very important task in many large musical schemes. Accordingly, literatures offered verities of approaches. Starting from 1st algorithm for detection of tonality by Longuet- Higgins and Stedman‟s procedure; it was based on comparison tones of tonal region of major and minor keys.

Later, based on this idea a better algorithm has been developed by Krumhansl Schmuckler for tonality detection. This algorithm was based on the distribution of weighted pitch class according to the duration of 24 major and minor scales profiles. Through after time other algorithm has been developed(Levine, 2015).

Some years later a work of scale recognition of a music has been existed by Zhao and Andres (2016). The algorithm they proposed to identify the scale of the melodic monophonic was without modulation and with any intervallic structure. The scale was based on twelve-tone equal temperament with the interval structure of 1, 2 and 3 semitones; with the capacity to detect 11124 varieties of scales.

Like some other works extraction patterns of music, contours of music notation, and cryptograms of music; they used musical coding systems since it is very important in representation(Eduardo, Salazar, & Liang, 2016)(Dutta, Kumar, & Chakraborty, 2013).

The detection of the scale was executed via nodes of deterministic walk in a graph. On this graph each edges maps the possible transformation and each node represents an intervallic structure, each transformation based on mapping edges which may contains intervallic structure when its intervals were appropriately portioned.

When the tested data they used was based on the melodies from both popular and seldom used scales.

The scale could be represented via interval structure and the elements which indicate the intermission among successive notes that make sure the scale. For example the interval vector of the major scale is (2, 2, 1, 2, 2, 2, and 1) and for minor scale (2, 1, 2, 2, 2, 1, and2). So, the scale was entirely built based on intervallic vector, mode, and tonic.

The scale was basically encoded based on six values, which were arranged in a vector c, called the scale code vector. If we go and a visit some of selected scales and forte number (table 2.2), we got some scales are not listed.

Table 0.2: some selected code scales (Zhao & Andres, 2016)

Why not pentatonic scale has not got a list in a table above at list with absent of the scale code? When we thoroughly look and get observe over table 2.2 the scales are encoded based on some variables.

Table 0.3: some variable value used by scales encoding algorithm (Zhao & Andres, 2016)

The algorithm governs collectively the interval vector of a set of notes and then analyzes its structure. To determine the scale of the structure it was based on deterministic walk in graph via rules nodes of the graph of valid arrangement based on 19 nodes and with each node interval S, T, or Tm as shown in (Figure 2.4 blow).

Figure 0-6: relationship between structure s, t, and tm(Zhao & Andres, 2016)

So, this deterministic graph was worked based on how one can move from one structure to another passing through the intervals of t, and tm at shorter distance determined by time. Then, the modes and tonic were known by the path of searching evidences.

A. Is the figure 2.6 structure was involving all pentatonic structure? Due to this, has the capacity to recognize Ethiopian indigenous musical scale (since among the world it is a country which follows a pentatonic scale)?

B. Was it designed based on the range of different musical sound instrument? The test data they used was Finnish_folk song dataset this dataset is a noise free dataset. So, how to adapt the system if noise contain the music(P & Toiviainen, 2004)?

C. How to know the exact major/minor key, range of pentatonic scale? Why octaves are not considered in design?

D. The pitch detector they used was a frequency domain based which is cepstral method pitch detector(Levine, 2015);

Suggestions on the above questions:-

1. The approach they used was a rule base approach, revise and require some modifications on the set of rules they follow. For example: - when they set a rule based on scale code vector , Tm: represents the number of tone and semitone of the scale structure, may have

value in the range of 0<=tm<=4. Why specified with at most 5 tone/semitone need a brief description, for what specific purpose?

2. Since the pitch detector they used was frequency domain pitch detector which is cepstral method pitch detector such types of pitch detector has a problem in tolerating or adapt scarce or pity noises, they were forced to use finnish folk song (this dataset is prepared based on noise free audio).

3. Let‟s make sure the Ethiopian indigenous scales based on piano roll. # b # b # b # b # b # b C0 /D0 D0 /E0 F0 /E0 G0 /A0 A0 /B0 C1 /D1

C0 D0 E0 F0 G0 A0 B0 C1 .

0 1 2 3 4 5 6 7 8 9 10 11 12 Figure 2.7:- notes with their index number (class)

Based on figure 2.6 and referring figure 2.7 let us locate the required indigenous pentatonic scales below.

Figure 0-7: diatonic and pentatonic scale

Figure 0-8: pentatonic scale

The intervallic structure for each scale, based on figure 2.6

Table 0.4: validity checking of Ethiopian Pentatonic scale with via deterministic graph Scale Name Tone arrangement Note class Graph relationship path Validity No (based on node number) 1 Tizita Major 1, 1, 1½, 1, 1½ 0,2,4,7,9, 12 19,17,15,11,12,6,7 Yes 2 Tizita Minor 1, ½, 2 , ½ , 2 0,2,3,7,9, 12 18,15,11,5,6,7 Yes 3 Bati Major 2, ½, 1, 2, ½ 0,4,5,7,11,12 18,15,10,11,-,- No 4 Bati Minor 1½, 1, 1, 1½, 1 0,3,5,7,10,11 18,15,10,11,12,-,- No 5 Anchihoye lene ½, 2, ½, 1½, 1½ 0,1,5,6, 9,12 19,17,18,15,10,4,5,6,7 Yes 6 Ambassel ½, 2, 1, ½, 2 0,1,5,7,8,12 18,15,10,11,5,6,7 Yes 7 Selamta 1, 1½, 1, 1, 1½ 0,2,5,7,9,12 18,15,10,11,12,6,7 Yes

Based on table 2.4 and the designed model (based on directed graph); since representation of note number 11 is missed from the graph any pentatonic scale or some other scales that are going to terminate with octave 11 the system incapable to recognize and identify the scale for example Ethiopian Bati Major, Bati minor incapable to recognize because they are terminate with note number 11.

Chapter Three

Methodology

Design and Development Instrumental Song of Saint Yaredic Chant Derived

Automatic Pentatonic Scale Identification: Begena

3.1. Overview of Music Scale Identification

Now a day, study and research at the area of multimedia and technology is running fast. Professionals, researchers, and experts are not in rest. Instead they are working diligently. That‟s a way we see at every morning, at every week, at every month and at every year we see a new and amazing thing.

Recently due to machine learning approach lot works that were unimagined to develop was now they got solutions. With learning algorithms like KNN, ANN, SVM, Deep Learning, and etc. at least with some basic features classification, prediction and etc. would be possible with some accuracy.

Rule based is an approach that works in classification, identification, recognition, prediction and other related works; based on a collection of rules, facts, and an inference engine. These rules can be in the form of relation, recommendation, directive, strategy, heuristic and facts are evidences, proofs, and truths which are stored in a database for inference use. An inference engine maps the rules with the knowledge or fact. So, according to the problem domain following such typical approach and devising, designing or developing the required model is an important thing.

Generally, this chapter explicitly discusses the design, process and practical procedures of Ethiopian lyre Bagana Scale Identification with modes of saint yaredic derived chant, preparing dataset based on world acceptable standard formula for pitch (i.e. pitch frequency for music, sound and vibrating related objects), collecting data that are going to be tested, devise an algorithm based on rules and facts, develop scale identification model correspondingly shows major or minor key, and range of the song. All these are important sequences for doing practical implementation.

3.2. Design Methodology

Approaches according to the application domain, and subject area there are different approaches used for different field of study. Since this research title touches multiple area of study (such as digital signal processing, multimedia, music and speech recognition) in the proposed model it uses more than one approach.

As it is discussed in Chapter-2 in Section 2.4 to detect or extract pitches from digital signal processing a comparison has been made between some selected time and frequency domain like ACF, CCF, SHS, CAF(Cepstrum + Autocorrelation) and PYIN. To correlate the collected tested data with prepared dataset rule based approach is preferable; and to identify the scale based on range, major/minor key require the collection of these two approaches and algorithm. The combination of these algorithms‟ results the model shown in Figure 3.1.

3.3. Architecture of Instrumental Song of Saint Yaredic

Chant Derived Automatic Scale Identification

The Ethiopian Lyre Bagana song Automatic scale Identification system primary require Begena song audio file as input and using pitch detector extract pitch frequency from audio file and then using a designed model an identification process would be performed(Figure 3.1).

Figure 0-1: the architecture of Begena song pentatonic scale Identification

As we have explained in Figure 3.1, this Identification process can be done using rule based approach. These sections discuss data, data preparation, and taking sample audio frame, pitch extraction, and define rules, model and testing data.

Data is a very crucial thing for research and analysis, according to their category and application domain there are different types. Audio data type is among which mostly used for sound, music, speech and related works. In case of our study we used for music purpose only.

Music data is presented in the form of monophony (melody), homophony, and polyphony. Melody music in case of Ethiopia Lyre Begena song is bounded major / minor and octave. The song of singer pitches goes with the melody of Begena plucking.

Dataset Preparation

According to the problem domain, data are collected from different resource regarding to its relevance for the required/specified research. So, the dataset prepared for this research work was based on world music and musical instrument tuning and notes standard(Hilbish, 2012).

Data Collection for Testing

The data is collected from variety of sources, online YouTube released Bagana song, and Bagana Training Institutes (like st,yared spiritual Bagana Training giving center, Sisay Bagana music and traditional instrument training center, and St.Dawit Bagana giving center). Every Bagana songs which are collected from these are primarily checked or annotated by scale or zema instructors and experts. So, the Data collective method is purposive.

Table 0.1: Source of data

Name of Training Institute Audio files No 1 Bahir Dar St.Yared Bagana Training Institute 883 2 Debre Tabor St.Yared Bagana Training Institute 7 3 Addis Ababa Sisay Bagana Traditional and 12 Spritual Instrument 4 Gonder St.Yared Bagana Training Institute 400 5 Fnote Selam St.Yared Bagana Training Institute 12 6 Youtube Online Bagana Song 43 Total 1357

From Table 3.1 the data was collected based on the specified quality with different and the same range, major and minor key of the scale of the song.

The reason why data was collected from different place is since Bagana was affected by environmental air condition.

Depend on this concept we plan to get data from these training institutes and media. However, from those training institutes they don‟t have any stored data, they play the instrument to train and learn the skill; but the training is not recorded. The data stored is only for testing students whenever reach in chapter of tuning Bagana. When they are approaching to graduating class they are expected to tuning Bagana by listening other singer‟s song. As a result, we couldn‟t get scale identified data from these sources instead we collected Bagana song data only that can be used for identification modeling.

Data Verification

The quality of scale identification is depending upon the quality of the audio data. The collected tested data is as much as possible contain free or moderate of noise. Since from the audio data pitch frequency is extracted through pitch detector. Therefore, the present

36 of noise above the threshold brings the pitches to be confused. Loose of confidentiality to perfectly say the exact frequency value of pitches.

If noise in the spectrum of signal creates peak it considers as valid pitch pinpoint. So, the presences of free or moderate of noise help for pitch detector to detect the exact pitches frequency value.

Steps Undertaken

To accomplish scale Identification process of Ethiopian indigenous pentatonic scale, it needs to fulfill the main steps. The process organized looks like the following steps.

Software Tools and Installation

To get successful completion for this research work there are software tools which support simulation and analysis; the tools used in this study are Audacity, Total audio convertor, stepvoice recorder, Microsoft office excels, and Tony/Praat. All these software tools were going to be installed on windows operating system.

Audacity

Audacity is an open source software tools, it is a versatile tools which can be used for recording, editing of sound, speech and audio files, and splitting or merging audio files, amplify, change speed, change tempo, high pass filter, low pass filter, notch pass filter (these three filter used to trim frequencies outside the specified range), noise reduction and removal, conversion from one audio file format to another file format however conversion is only to some common audio file format such as wav, aif, ffmpeg, mp3, ogg, and flac. It is publically available at (Audacity, 2018).

Total audio Convertor

This software tool has the capacity to support over 32 audio file format, almost with various file formats available on the market. So, for our work it is very import if the tested data has been occurred with different file format this software tool has a solution. Meanwhile, the pitch detector software tool is bound with some countable audio file format (cool, 2017).

Stepvoice Recorder

To record the song we used stepvoice recorder; that encodes mp3 files directly and easy to adjust the recording volume automatically(Andrey, 2016).

Tony

Tony is a software tool for high class an interactive scientific pitch and pitch transcription. This tool provides: (1) it uses an algorithm which fills a state of art for music note and pitch transcriptions and estimation. (2) Easy error noticing for visual and auditory feedback. (3) Good user graphical interface on which users can quickly precise estimation errors. (4) Wide-ranging export function allowing additional processing in other application.

Microsoft Excel

Microsoft excel is one of a software package of Microsoft office most widely used for business related transactions, analyzing, searching and storing data, make our work easier, and usage of mathematical formula makes easier(Harshita, 2018). Now a days Microsoft excel is expanded to verities of services, possible to create GUI, possible database connectivity with access database, analysis in related to security and cryptography research such as symmetric and asymmetric key encryption and decryptions. Generally it has hosted in a way to analysis scientific experiment. So, for our research experiment excel is our primary option.

Microsoft Visual Studio C#

Csharp(C#) is one of a Microsoft product, powerful and simple programming language primarily aimed at designers and developers making applications by using .Net Microsoft framework. Most of its features inherit from visual basic and C++. However, with some of the inconsistencies and anachronisms, resulting in a help and more logical programming language; it was publically existed in 2001 with version C# 1.0.

The reason why we prefer this programming language was its simplicity, provide remarkable graphical user interface (GUI), flexibility or dynamicity, portability,

38 integration with other software, prevalence of domain experts are active online. Whenever difficulties are occurred solutions are not far.

Dataset preparation

Chordophone instruments are adjusted based on equal temperament system of adjusting where each octave of notes is divided into equal size of twelve semitones or notes # b # b (Rossing, Wheeler, & Moore, 2002). These semitones are C0, C0 /D0 , D0, D0 /E0 , E0, # b # b # b F0, F0 /G0 , G0, G0 /A0 , A0, A0 /B0 , and B0. These notes are revived as one octave. For instance, the semitones/pitch frequencies 16.351Hz, 17.32Hz, 18.35Hz, 19.445Hz, 20.60Hz and etc.

Other pitch frequencies can be calculated based on A=440Hz as reference in equation

Equation 1: f=2(d-69)/12*440 ………………………………………………………………….(1.1)

Where f is the frequency of the pitch and d is a relative pitch number where d is 69 frequency A =440Hz.

Pitches are offered names that correspond to their relationship with regard to how many semitones follow and precede them. The relationship can be measured in cents, where a cent is a logarithmic unit of measurements where the distance between each semitone is 100 cents (Rossing et al, 2002). The size of the cent interval can be calculated using equation 1.2; where f1 is a frequency corresponding to a note and f2 is the frequency of a note whose distance from f1 n measured in cents.

Equation 2:

(f1/f2) n=1200.log2 ……………………………………………………………. (1.2)

To calculate the distance between semitone, we are expected to use equation (1.3) below

Equation 3:

Ds=fi+1 -fi ………………………………………………………………………………………………………(1.3)

Where Ds distance between semitones, fi+1 succeeding of pitch, fi preceding pitch

The possible half centos of each pitch would be calculated as scripted equation (1.4)

Equation 4: Hc=Ds/2 ………………………………………………………………………(1.4)

Where Hc represent half centos,

Ds distance between each semitones

To calculate the maximum boundary of each possible pitch could be calculate as show below (equation 1.5).

Equation 5: Pmax=f+Hc ……………………………………………………………………(1.5)

Where Pmax maximum boundary of each pitch frequency,

f exact each pitch frequency, and

Hc centos

To calculate the minimum boundary of each pitch frequency there is a formula to compute as shown below (equation 1.6).

Equation 6: Pmin=f-Hc …………………………………………………………………....(1.6)

Where Pmin minimum boundary of each pitch frequency, f exact each pitch frequency Hc centos

Audio Adjustment

Adjustment refers to any types of technique executed on raw data to arrange it for another processing scheme. Any audio data may be embedded with noise, interference, and other undesirable components in collected data. Whenever much noise has been present within audio data, pitch detectors would get difficulty in pitch extractions or detections. So, in order to know or identify the perfect scale of the song, we have to clean the noise. Therefore, we used audacity tool to remove noise and outliers.

Procedure 3.1 adjustment of the collected data for testing

Input: any Ethiopian Bagana song audio file with supported file extensions.

Output: produce result with adjusted, noised removed or audio noise reduced audio file

1. Read audio file 2. Sense audio noise spectrum through audio file 3. If noise present through excerpt of audio file 4. Apply noise removal 5. else if noise to the whole audio file 6. Apply noise reduction 7. else 8. Stop from applying noise removal and reduction, prepare as it is

Sample Frames Taken from Audio (sampling audio)

To analysis music and basic elements and components of music, sampling time of an audio for study is not more necessary to exceed 30 second. 30 second audio sample is quite enough. For example in analysis of music genre classification, timbre, rhythm and other music related works sampling audio time they take 5, 10, 15, 20, 25, and 30 seconds was taken. In case of our study we made minimum 5 second.

According to fluctuation and speed of pitch frequency through song, these specified time intervals have great significant. The scale of the song is known within 5 second.

Audio frames of the first e.g.30 second

Audio frames of at the middle e.g. 30 second

Figure 0-2: audio Bagana song of archi singer’s alemu aga “manyimeramer” The audio in the above figure is taken from YouTube. The sample audio frame has been trimmed.

From the above figure 3.4 the audio has been taken from online YouTube, the sample audio frame has been trimmed at the beginning of audio with 30 seconds (starting 0 up to 30 seconds), at middle audio with 30 seconds (which is from 4:19 seconds up to 4:45 seconds). For more detail work in relation to the impact of pitch occurrence for the identification of scale, it is detailed in chapter 4(experiment and result).

Pitch Detector Algorithm Selection

Before pitch extraction has been conducted, it is better to scientifically make sure that; what in chapter two literature review Section 2.4 has been discussed. Already pitch detection on varieties of music and speech databases have been experimented and shown with their accuracy and error. Which pitch detector algorithm and tool is preferable for Bagana pitches? We model pitch comparison in Figure 3.3. Based on literature review,

42 literatures recommend time domain approaches for monophony music analysis. Due to this reason we select time domain approaches from time domain also there are lots of algorithm based on the state of art PYIN is selected. However, what experimental result looks like in Bagana Song pitch detection? Is the same with the literatures has shown before or brings unique result? To answer the above questions, we have designed a pitch detector model (the detail is presented in Section 4.4).

ACF Collected CCF Apply Pitch Bagana SHS Detection on audio pitch CAF dataset PYIN …..

Check validity of pitch

List each algorithm’s Standard Show Accuracy pitch dataset Result

Figure 0-3: pitch comparison diagram

Pitch Extraction Methods from Tools

As we have described in Chapter 2 literature review 2.4.1, for this research we used Praat and Tony software tools to extract pitches based on the plugins they have. CAF, ACF, CCF, and SHS pitch detector algorithms are extract audio pitches using Praat. Whereas, PYIN pitch detector is extract audio pitches using Tony. Have a look at the steps to extract pitches using Praat and Tony respectively.

 Steps to extract pitches using praat:- A. CAF

i. Open praat then open and import audio file ii. Go to the right side and press View & Edit button

Figure 0-4: pitch extracted using CAF

iii. From Figure 3.4 select audio spectrum and pitches then goes to Pitch Menu iv. From Pitch sub menu press Pitch listing

Figure 0-5: Extracting pitches and displaying with notepad file format v. After the file has been extracted save with file notepad or excel file format and prepare for analysis.

B. ACF i. Follow CAF step i ii. Then Go To right side menu and press analysis periodicity iii. Next press to pitch (ac)…. You will see bellow dialog box settings

Figure 0-6: Praat ACF defaults setting iv. Based on Figure 3.6 press OK button then return back to the right side sub menu of the main interface then press analysis button, next press to pinprocess button , and then synthesis v. Finally follow CAF step ii, iii, iv and v

C. CCF i. Follow CAF step i ii. Follow ACF step ii iii. Next press to pitch (cc)…. You will see a dialog box like Figure 3.6 with another setting iv. Finally follow ACF steps iv and v

D. SHS i. Follow CAF‟s step i ii. Follow ACF‟s step ii iii. Next press to pitch (shs)…. You will see a dialog box like Figure 3.6 with another setting iv. Finally follow ACF‟s step iv and v

 Steps to extract pitches using Tony:- A. PYIN i. Open Tony software tool ii. Then go file menu and open audio file; you will see audio file spectrum, pitch track and spectrogram which are used for analysis (Figure 2.7)

Figure 0-7: Tony software tool interfaces with audio attributes 3.3.1. Pitch Extraction

When we extracted pitches from song, we have to take sample of an audio data with at least 5 second or with at most 15 minute at the beginning, excerpt, middle or ending of song.

As it is mentioned in the above section, YIN is a state-of-the-art algorithm to detect pitch; moreover, some believes PYIN performs better. Although. YIN or PYIN is prominent pitch detector; it needs to be compared with other similar algorithms. The experimental result is presented in the subsequent chapter. Pitches are scattered to surrounding as Bagana strumming shown on Figure 3.8.

When Bagana strummer plucking Bagana, the song sound go out to the surrounding, and any listener sense its color of the song and the musician identify the color of the music with its scale. Whenever the musicians sense the music, they carefully sense the pitches come from the instrument based on the distance between pitches (tone) the scale is going to be identified. PYIN algorithm resembles to sense the pitches like human capability.

F Db

Db b

C# # C b C# E

b A G D

Bb C# D

B E

C# G Ab

Figure 0-8: when a man Strumming Bagana, pitches are emanate and go out to surrounding

3.3.2. Approach

Rule-based algorithm with the assumption of 12 (twelve) notes (semitones) temperament of chromatic system performs by taking the general music note standard considering the individual note names independent of musical pitch dataset suggested by the stored data in MIDI file format. The typical MIDI file prearrangement offers two types of

information which were going to be used by the algorithm; (a) the DT (delay time) and (b) note on instruction(Matthias & Simon, 2014). dt indicates the delay of time in between MIDI events and note on instruction indicates note‟s onset or instruction termination (the MIDI note with zero velocity), these two basic information‟s were come together with embedding the pitch frequency extracted from the pitch detector tool that we used. So, for this study the two basic features (pitch frequency and their time) is very important.

3.3.2.1. Counting Pitch occurrences:

Check frequency present In dataset Maps to Frequencies Pitch Maps to notes

Selected pitches with in the audio

…… C0 ….. B0 C1 …… B1 C2 D2 …...

…… Occurance ….. Occurrence Occurrence …… Occurrence Occurrence Occurrence …… & timing & timing & timing & timing & timing & timing

Figure 0-9: pitch counting procedure

Procedure:

1. Read adjusted audio 2. Extract pitches frequency from audio 3. Convert pitch frequency to pitch note 4. Cross check the extracted pitch note with dataset 5. Count each pitch note occurrences with their timing Example: Sample Bagana Song Audio appendix 3, Table 0.1

The appendix 3, Table 0.1 show us from a Bagana song a sample audio data has been trimmed and taken from the well know Ethiopian Bagana singer; arch singer alemu aga (song name Tew simang agere) from this song for about 51 second and 060657596 has been cropped from total audio track that takes 6 minute 55 second 915 microsecond and pitch occurrence has been counted from total audio 128 pitches about 57, 23, 22, 5, 20, and 1 occurrences are C, D, F, G, A and A#/Bb pitches respectively.

3.3.2.2. Ranking Pitches:

Pitch occurrence Read pitch and 2 Preprocessing Module Occurrence (Sorting and 3

indexing) 4

Figure 0-10: give rank for each pitch based on their occurrence

Algorithm:

a. R: rank b. P: set of pitch name(notes) p, and pitch occurrence po

c. Procedure putRanking(Pth,Ri)

d. Pri← list of pitch occurrence taking Ri as pitch name

e. Iri← list of pitch name ranked/selected by Ri

f. Construct Ri‟s pitch name linkage using Pth

g. For each pitch name i in Iri do

h. NRui← list of Ranked which selected Ii

i. For each ranker r in NUui do

j. u is in Pri use the ranker and pitch name of u k. end for l. end for m. end procedure

For example: take appendix 3 table 0.1 for ranking. Based on the given three parameters like pitch name, pitch range, and occurrences in appendix 3 table 0.1; assigning rank for each pitch appendix 3 Table 0.2. 3.3.2.3. Select top pentatonic pitches based on pitch

set order:

Why top 5 pitches? Since Ethiopia indigenous scales are chanting using pentatonic macro pitches.

Pitch order set

Ranked pitch module P1 Take as input Compare Maps P2 and sort P3

pitches . pn

Figure 0-11: top 5 pitch selection

Procedure

i. Read Rr , i, j=0, Res, L ii. Rr← ranked pitch set module iii. i ← index of ranking pitches iv. j← counter of top 5 pitches v. Res← top 5 pitch holder vi. Procedure topPentatonic(Rr, I, Res) vii. For i ←0 to N do viii. Compare Rr[i] with Rr[i+1] //comparison among successor with predecessor ix. if R of i is greater than R of i+1 // whenever the predecessor is greater than the successor, Res[i] provide a space for predecessor. x. Res[i]=R[i]; when j=5 goto E xi. else xii. Res[i]=R[i+1] // other ways the R[i] switches for successor xiii. end if xiv. end for E: xv. end procedure

For example: take table 0.2 for selecting top 5 pitches, to see method of selecting take a look at appendix 3 Table 0.3.

3.3.2.4. Revisit arrangement:

When a very speedy plucking of Bagana is performed, each Bagana strings of pillar create another or unique pitch sound and tone. Since, each pitch detection time has specified; within this bounded time applying other frequencies (noise or speedy attachment of strings) have the capacity to change the original pitch and its range as well as its scale. So, sensing and set threshold is another preferable way in adapting the identity of pitches present within an audio.

Range

Module Ranked pitch module

Notes Out of Ci j D b RangePitches >=5 || Pitches >7 Pentatonic i Di pitches <=6 Song b Ei j+1 Ei Fi b j+2 Gi Gi . Top fifth b Top 5 pitch (same or Ai pitch difference occurrence) Ai . b candidates Bi . Bi

Ci+1 b . Di+1 D Compare the 5th pitch based on pitch i+1 . E b timing T, and pitch set order Po. i+1 Ei+1 . (Po && T) or (Po) Fi+1 . . . . Calculate Distance/Tone . j+n Pi+n

Sorting the range of pitch order

Figure 0-12: rescanning pitches either they are pentatonic category or not

______

Procedure:

a. Rescan Rpm, j, k, r, Pt, and Po, and Res b. Rpm← ranked pitch module c. j ← index of ranking pitches d. k← counting top n pitches e. r ← range of pitches f. Pt← pitch timing g. Po← pitch order h. Procedure_0 revisit_Arrangments(Rpm, j, k, r, Pt, Po) i. For j ←0 to k do j. if k = 5 k. call topPentatonic(); l. else if k >5 && k < =7 do m. Rpm[k-1]=Rpm[k] //when there is a candidate key with the same occurrence n. Is (Ptt[k-1] > Ptt[k] )? yes o. Po[j] =Rpm[j] // the pitch set order or indexed pitch reserve the space of range of melody Pt[j]=Ptt[j] If Po && Pt Res[j]=Rpm[j] else Res[i]=p0[j] p. else q. if the pitches are more than 7 it is impossible or no more confidence to say pentatonic scale, r. End if s. end if

t. end for u. end procedure_0

For example: let‟s have a look at the following appendix-3 table 0.4, by taking Bagana Singer Abel Tesfaye song name “moten bemote gedleh” sample audio time taken with 33 second.

Based on the appendix-3 table 0.4 whenever the pitch occurrence and rank has been existed the same, we are then expected to look over their time wasting on pitch else honoring pitch set order.

3.3.2.5. Measuring distance between pitch and

identifying the scale:

After each sup-process performing such as pitch extraction, pitch mapping with dataset, counting pitch occurrence, ranking pitches, selecting top pentatonic pitches based on pitch set order , revisit arrangement and the last step to identify the scales is measuring the distance between each ordered or arranged pitches.

Figure 0-13: song scale identification Model

Procedure:

a. read adjusted pitches, index b. calculate distance c. mapping adjusted pitches with index d. showing the major /minor key e. cross checking the scale formed with scale dataset f. if the scale is in dataset Show the scale with its range of the scale g. else Out of the known pentatonic scale or it is category of other scales

Let‟s have a look at appendix 3 table 0.5 , The final identification would be as indicated in at appendix Table 0.5 it shows that; after the well-adjusted pitches are prepared or completed; the distance between pitches (tone) would be calculated. Based on the tone the scale of the song would be identified. So, for example it is found that Abel Tesfaye‟s song entitled „moten bemote gedleh‟is identified as Chenet Kignit.

Chapter Four

Experiment and Result

4.1. Introduction

This chapter embraces the experiment of Ethiopian Lyre Begena Song Scale Identification as explained briefly in chapter three. This chapter covers the following preparation of training and testing dataset, pitch detection, prototype development, simulation, result and discussion.

4.2. Data Preparation

As illustrated in chapter three the data for Ethiopian Lyre Bagana Song Scale Identification is prepared according to the world wide pitch preparation standard.

The incorporated attributes within this pitch preparation standard are pitch (name, range, frequency). In addition half semitone, cents1, semitones, index, exponent and with the two standard MIDI file types; the delay time (dt) and the note-on instruction. For more detail let‟s have a look at Table 1.0 bellow.

The range for the training dataset we have prepared was from C0 up to C13. However for testing dataset we have recorded from C0 up to C4. The reason is Begena Maximum string spreading capacity (threshold) is going up to C4. Beyond this threshold, the string is going to be censored. The pitch dataset was prepared as shown in Tabel 4.0.

1 “Cents “ it is a logarithmic unit of measure of an interval, and that is a dimensionless ”frequency ratio” of f1/f2

Table 0.1: prepared training pitch dataset format

4.3. Testing Data

The tested data was collected from various Begena Training Centers. More than 1357 varieties of audio melodies and online audio Begena song was collected, the scale of the melodies was annotated by archi Begena singers and teachers with the same and different octave as well as with different ranges with major and minor key.

4.4. Pitch Detection Comparison Result

Pitches are extracted using praat (CAF, ACF, CCF and SHS) and Tony (PYIN) and then a comparison has made on extracted pitches with world standard pitch dataset. The result is Based the error detected per octave the result is recorded and analyzed in below Table 4.1.

Table 4.1 error detected per octave of pentatonic scale (sample taken from experiment)

No Scale type Audio file name CAF ACF CCF SHS PYIN 1 Bati Major Rec1107-050533 1/5 1/5 1/5 2/5 0/5 2 Bati Major Rec1107-051004 2/5 2/5 2/5 1/5 0/5 3 Selamta Rec1107-051020 1/5 1/5 2/5 1/5 0/5 4 Selamta Rec1107-051031 1/5 1/5 1/5 1/5 0/5 5 Wanen Rec1107-051044 2/5 2/5 1/5 2/5 0/5 6 Wanen Rec1107-051137 1/5 1/5 1/5 2/5 0/5 7 Chernet Rec1107-051152 2/5 1/5 1/5 1/5 0/5 8 Chernet Rec1107-051607 1/5 1/5 1/5 2/5 0/5 9 . Rec1107-051718 1/5 1/5 1/5 2/5 0/5

From the above Table 4.1 each row and column indicate us for example:- No representing for listing numbers, scale name for typical pentatonic scale like Bati Major, Tizita Major, audio file name rec1107-050533, and error detected by each algorithm for instance by CAF, ACF, CCF, SHS and PYIN would be 1,1,1,2 and 0 error per pentatonic scale respectively.

About 713 thoroughly recorded Bagana string pitches in audio file format has been collected in the form of melodic pentatonic scale. From them CAF, ACF, CCF, SHS and PYIN the accuracy would be 72%, 74%, 76%, 68% and 98.4 respectively. Based on the counting experimental result PYIN is selected for scale Identification.

4.5. Prototype Development Using Integration of Microsoft Office Excel and Visual Studio C#

A prototype used to test the working and complexity of the devised algorithm has been implemented in Microsoft excel, Visual C# and Sql Database. It also used to test and evaluate fully identification properties of Lyre Song Scale Identification. Figure 4.1 below shown Microsoft Visual Studio Professional 2015.

Figure 0-1: Microsoft Visual Studio Professional 2015

4.6. Prototype Interface and Operations

The developed Prototype incorporated three major modules these are:- a. Load Audio and Extraction of Pitch frequency b. Perform Computation c. Check Scale

Let‟s take a look at Figure 4.1

Figure 0-2: Prototype Implementation Design 4.6.1. Loading Audio and Extraction of Pitch

Frequency

Well-adjusted and prepared audio data and then feed to the system in order to get the desired pitch frequency. Since, pitches are the fundamental, reference or the basement for the identification of the song. We have been integrated Tony Software Tool with our Prototype for the purpose of extracting pitch frequency (see figure 4.2).

Figure 0-3: Loading Audio and extracting pitch Frequency

4.6.2. Perform Computation

Based on the procedures and algorithms described in previous chapter, computation is performed with the help of Microsoft office excel software tool. So, at this stage Microsoft visual studio C# integrates with excel to see the computation. The computation

64 is performed starting from pitch input up to measuring distance between ordered pitch set.

Let‟s take a look at Figure 4.4 bellow.

Figure 0-4: procedure and algorithm computation 4.6.3. Scale Identification Interface

After the distance between pitches is computed the tone is known. Next, by taking tones of pitches as input cross checking with database is made. Finally identify the major/minor key, range, and scale of the Lyre Song. To do this process, first press navigate file, then

65 select the required input attribute file and last press identify button finally it notify the scale through dialog box. Let‟s take a look at Figure 4.4 bellow.

Figure 0-5: Scale Identification Interface

4.7. Results

In this section, the proposed method is applied to identify the Ethiopia Lyre Bagana Song scale of monophonic melody without modulation. Specifically, the goal was to identify the scale, the range, major and minor key of the scales of melodies of instrumental song of St.Yaredic derived pentatonic scales via Ethiopian Traditional Lyre Bagana song.

The data collection distribution method looks like bellow:-

 Data collected in the form of melody for pitch detection  Solo song( song of one typical Bagana singer)  Vocal Melody Range(VMR) > Bagana Melody Range(BMR)  Vocal Melody Range < Bagana Melody Range  Real censured and un-criticized Bagana song  Bagana melodies only

Large amount of tested data was collected from Bahir Dar St. Yared Bagana Training Institute. The reason why much data has been collected from this Training Institute was because beyond teaching or training services; historical preparation of Bagana is constructed. When we see over its amount of data collection; about 883 short melodies (with time interval ranging of minimum 5 second up to maximum 1:30 minute) is recorded out of which 50 long melody ( from 1:30 minute goes up to 15 minute). Form 120 solo song 60 of which was Singer accompanied song. Which mean range of Bagana is greater than range of singer (BMR > VMR) and the remaining 60 solo was Range of Bagana is greater than range of singer (BMR < VMR). With very caution recoding about 713 melodies was collected primary used for pitch detection with slow plucking time; in addition to this about 43 from real released censured and uncensored Bagana songes has been collected. However, only 807 melodies have exactly made tonality information with database. For detail information let‟s have a look at previous chapter 4 Table 4.1 .

Table 0.2: correct and incorrect scale identification table No. Data Collection Distribution Annotated Scale Audio File Correct Incorrect 1 Data collected in the form of melody 713 699 14 for pitch detection 2 Vocal Melody Range(VMR) > 60 2 58 Bagana Melody Range(BMR) 3 Vocal Melody Range(VMR) < 60 56 4 Bagana Melody Range(BMR) 4 Real censured and un-criticized 43 31 12 Bagana song 5 Bagana melody and song 481 436 45 Total 1357 1236 119

Table 0.3: correct and incorrect scale identification table

No. Data Collection Distribution Percentage Scale Correct (%) Incorrect (%) 1 Data collected for pitch detection 98.4% %1.60 2 VMR > BMR 3.34% 96.67% 3 VMR < BMR 93.34% 6.66% 4 Real censured and un-criticized 72.09% 27.01% Bagana song 5 Bagana melody and song 90.64% 9.36% Total 91.23% 8.77%

800

700

600

500

400 Annotated Audio File 300 Correct Scale Incorrect Scale 200

100

0 Data collected VMR > BMR VMR < BMR Censured and Any Begena for pitch un-criticized melody and detection song 1 2 3 4 5

Figure 0-6: graphical representations for correct and incorrect scales of melodies Based on Figure 4.7 a high accuracy shows on graphical representation was on data collected for pitch detection. The remaining attributes more or less errors have been shown.

Table 0.4: pitch occurrence and average wasting time for pitches No Types of strumming Audio File Correct scale Incorrect scale 1 Single strumming 921 882 39 2 Double strumming 436 356 80

Finally based on the data show in Table 4.2 or Figure 4.2 measure the result obtained total accurate (known scales) and incorrect (error or unknown scales) data let‟s have a look at bellow equation 4.0 and equation 4.1 respectively.

Equation 7 Accuracy = sum of each accurate data * 100 ……………………equation 4.0 Total Data

Accuracy = accurate (BahirDar + Gonder + AddisAbaba + Debretabor + fnoteselam+online) * 100

Total Collected For testing Data

Equation 8 Error =100- Accuracy ……………………………………………………..equation 4.1

So, based on these above formula equation 4.0 and equation 4.1 let‟s evaluate the final result bellow.

Accuracy = 713 + 56 + 2 + 31 + 436 = 91.23%

1357

Based on the provided collected data the accuracy shows 91.08%

Error=100%-91.08%= 8.77%.

Accuracy result for style of strumming based on equation 4.0 and equation 4.1

Accuracy for single style = Amount of single style strumming audio file *100 Total single style collected Data

Accuracy for single style = 893 * 100 = 95.8% 932

Error of single style = 100%-Accuracy for single style Error of single style = 100%-95.8=4.2%

Accuracy for double style= Amount of double style strumming audio file * 100

Total double style collected data

Accuracy for double style = 356 *100=81.65% 436

Error of double style = 100% - Accuracy for double style Error of double style = 100% - 81.65%=18.35%

Table 0.5: Accuracy result summary No Audio type category Accuracy Error 1 Single strumming 95.8% 4.2% 2 Double strumming 81.65% 18.35% 3 Total audio records 91.23% 8.77%

4.8. Result Discussion

Two experiments are conducted. Experiment-1 showed comparison between pitch detector algorithm, and experiment-2 scale Identification. In this section, we discuss the result obtained based on the evaluation metrics which are accuracy and error of scale identification experiment 2. As the result shows us about 91.08 % is accurate and with 8.91% inaccurate (unknown scales) from total collected Audio Bagana Songs. As Table 4.4 when we see the result on style of plucking single style of Bagana strumming about 95.8% was accurate and 4.2% inaccurate. Whereas , in double style strumming about 81.65% was accurate and the remaining 18.35% inaccurate. The reason why about 8.09%, 4.2% and 18.35% respectively the real known scale melodies the system consider as unknown scales is that; any music instrument according to its category (chordophone, aerophone, Idiophone, membraphone) there are factors affecting in it sound(Feng, 2012). As Bagana experts from their experience suggest that there are many factors affecting in pitch detection, range, knowing the major/minor key of melody, tones, and scales of the Bagana‟s Songs. These factors are based on natural and physical architecture of Bagana and there are some other scientific reasons in relation to Music Science. Let‟s have a look at the following scripted points. 1) Bagana String (ኣው ታር):- Nature of Strings: - the Bagana strings are made up of small intestine of sheep, cow or some other animal guts. The sound energy, frequencies that come out differ from each other. Thickness of the strings: - each and every string has a different position according to their thickness and thinness. 2) Tuner (መ ቃ ኛ):- this tuner has the capacity to bring the required pitch, tone, and scale by moving the tuner up and down. Recoil the string and the distance between the tuner and string, sliding tunner are the factors in changing pitch frequencies, tone and scale of the song. 3) Bagana timber (በገና የተሰራው እንጨት): - according to the wood made up of; the Bagana Pitch frequency, tone and scale has factor in changing their events. Example: - Bagana‟s body parts which constructed from one lane wood and different wood are not bringing the same color, pitch, tone and scale. 4) Birkuma (ብርኩማ ): - length of birkuma (its shortness and largeness) has a factor in changing the events. 5) Enzira (እንዚራ): - which is made up of cow or ox tannery, which is primary, used to adjust vibration. There are two event affecting vibrations these are: -

Thickness and, Position of Enzira. 6) Sound-Plate (የድም ፅ ሣጥን):- there are factors affecting in the pitch frequencies, range, and scale of melody/song. These are as shown below: - Plate Width, Plate Length. 7) Environmental Noise: - during recording melodies unexpected noise has introduced and recorded together with Bagana string sound(Rabiner et al., 1976).

Due to the above numbered and underlined factors the required accuracy has been reduced. During my experiment, i have ordered more than 7 Bagana strummer to song on some Bagana by the same range the result show us some pitches has been slacked, incorrect major/minor key, tones has been changed and the correct scale has been approaches to ambiguous and latter becomes unknown. When i visited the Bagana that I have left in the afternoon, and I met them after a day (one night) in the morning I found them some Bagana colors has changed. We pointed out the following hypothesis based on the experimental observation. i) Since the energy of each strummer was different sliding of tuner may occur. Due to this each frequency of Bagana strings vary (increase or decrease). ii) Environmental condition has also an impact when the environment hot the range and color kept as it was(Rabiner et al., 1976). Whereas , when the environment cold the color changed. So, the natures of string become shrinking or spreading.

When the scales identification performs its work; it is based two events these are: a) Pitch occurrences b) pitch occurrences and average wasting time.

When we see on the pitches preparation the data was safely collected. No additional pitches have been detected they were properly identified the scale of their melody for example: - audio collected for pitch detection the accuracy was about 98.4%. Whereas, on the data collected by archi singers and teachers and on some online released Bagana songs as experiment show us additional pitches has been appeared. This was because when speedy plucking or strumming of two strings at the same time; during this time another micro pitch/es and tone/s has/have been created. So to eradicate these additional micro pitch/es and tone/s we set timing threshold.

Chapter Five

Conclusion and Recommendation

5.1. Conclusion

In this research, we have proposed a model for identifying saint yaredic chant derived scales such as selamta(resemble geez), wanen(resemble ezil), chernet (araray) and bati major of melodies in comprehensive manner by the temperament of Ethiopian clerical orthodox tewahido typical lyre Bagana song. This method can detect both known and unknown pentatonic scales. Under the process of this study we have performed many tasks. Bagana melodies both single and double methods of strumming has been collected from the ground by collecting from different training institutes by teachers and archi singers of Bagana. The selection of Bagana strummers from the trainers were not limited by the age. However, by their capacity such as those who graduate by three scales, those who complete the three scales during training both by styles of single or double hitting. The collected audio file data both used for pitch detection and scale identification.

After that the model of the proposed system (“Ethiopian Lyre Bagana song scale identification system”) has designed. And a pitch frequency to pitch note conversion, count pitch occurrence and their timing model were developed for pitch ranking. In this study, we used two different experiments. For this study we used total 1357 different audio files 939 of them single style strumming and the remaining 436 in double style strumming were prepared for pitch detection and scale identification.

For simulating the result primarily we have done a comparison between some pitch detector algorithms from time, frequency and hybrid domain such as ACF, CCF, PYIN from time, SHS from frequency, and CAF from hybrid domain respectively. As a result, as PYIN was a better accuracy. By using this pitch detector pitch extraction has been done, and then using visual studio C# programming language graphical user interface has been designed for scale identification.

To standardized and facilitate the handling and naming of scales a numbering method was introduced as well as the respective pitch set order. For the prepared melodies database, the algorithm got appreciable result according to a typical category of audio files such as collected melodies for pitch detection we got 98.4%, VMR>BMR 3.34%, VMR

The main challenge we face for scale identification of Ethiopian Lyre Bagana songs was tracking micro tones, pitch detection (for newly constructed Bagana), and when VMR > BMR (unbalanced vocal pitch with instrument pitch was occurred). Micro tones were created when very speedy plucking has done. For example transition time 1/32, 1/64 and 1/128 nota due to this extra pitch have been found. So, in this study we have tackled to get the correct identifier of the range of scales with the same competent pitches by visiting on the time they waste else if their timing is the same pitch set order has introduced based on this the proposed algorithm end its task. Hence as we have seen from testing result, the identifier is displayed the exact and correct scales for melodies which were prepared for pitch detection and with very good accuracy for single style strumming. So, this is the core strength of this study. However, we have not used other mechanisms to handle the above challenges and this is considered as weakness of this study. Therefore, this is an open issue for future research.

4.9. Contribution

In this study the following contributions are figured out:-

 We have developed an instrumental song of St.Yaredic chant derived pentatonic scale identification on Ethiopian Lyre Begena. This model can use other pentatonic scale follower. It is possible to extend the model in the format to identify tetra-tonic, hex-tonic, heptatonic, diatonic, chromatic and some other scales.  Since scales of melodies are one sub micro features of music, it is possible to search, register or retrieve music information to/from YouTube. For example:-

According to the categories of world music scale most African countries are pentatonic scale follower, western part diatonic scale and Arab countries they used chromatic scales. However, within each category of scales there are lot modes of scales that uniquely identify the scale of the specified country. For example according to Dr.Ezra Abate (who is an Ethiopian Music qinit researcher) in Ethiopia currently there are 9 scales. These scales are all categories of pentatonic scale; Tizita Major is categories of pentatonic scale. However, its mode (tonal architecture) makes it unique throughout Africa and the remaining continent.  In music for musical instrument trainers and educators it has a great contribution in testing their performance in identification of the octave/range, major/minor key, tones and scale concept.  For music composers, professionals and experts help them for research and analysis in related to scale.  For these music scales sensitive religious institutions like orthodox tewahido, muslims and if there are some other religious institutions they can use by extending or modifying some re-arrangements.  It helps to music therapist in treating sick‟s and modes of normal person by uniquely identifying the specified frequency of melody. For example: - if the sick entail into grief/fear it is possible to turn into joy by setting/tuning the string frequency 396Hz.

4.10. Recommendation

The results of this research are beneficial and can be used in any areas of music scale identification applications. Scale identification of melodies are important when audio files are processed or filtered automatically for tasks such as knowing major/minor key, and range of song. Finally putting a direction as a roadmap for future work regarding to our work is important. So, this experiment needs further analysis using frequency domain approaches. The following main areas are recommended as further work for this study.

 As experiment showed us when VMR > BMR the accuracy approaches to none (3.34%). So, working in upgrading accuracy by using other approaches is our primary suggestion.

 Another thing that we faced during experiment was degrading the accuracy when a style of Bagana strumming was in double mood. By setting better threshold, some other methods or adjustment bringing to better accuracy is our second suggestion.

 Our major concern was identifying the scale of monophony Bagana song. How about for homophony, and polyphony Bagana style of songs or other songs is our third suggestion

 By using our model apply on other remaining traditional song musical instrument (e.g:- Masinko, Washint).

In General, as chant (zema) or music science this research work only focus to one sub features of chant or music which is scale. However, to evaluate the performance and summary of the song either it is good, un-recommended, acceptable, or unacceptable additional features of song/music are required like rythim, timre/color and some other features of zema toward Ethiopian Lyre Bagana song and to some other traditional musical instrument.

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APPENDIXES Appendix -1: Document related to some facts about Bagana

Figure 0-1: Emperor Haileslassie Plucking Bagana

Figure 0-2: alemu aga playing Ethiopian lyre Bagana song concert to white people

(a) (b) Figure 0-3: (a) Harp in Christ Church Jerusalem Israel (b) Hiding place played on the harp

(a) (b)

Figure 0-4: (a) Ethiopian megabesibhat Bagana strumming using Fingers method of

Figure 0-5: Bagana body parts.

Appendix-2 Saint Yaredic Chant Derived Pentatonic Scales

Figure 0-6: selamta scale arrangement on staff

Figure 0-7: Finger position on Bagana strings

Figure 0-8: Chernet scale arrangement on staff

Figure 0-9: Wanen scale arrangement on staff

(a)

(b)

(c)

Figure 0-10: (a) Selamta scale, (b) Wanen scale and (c) Chernet scale

Appendix 3: scale Identification exemplary steps

Table 0.1: Sample taken from arch singer alemu aga Bagana song (tew simagn agere song) with in 51 second and 060657596 microseconds and pitch occurrence.

No Pitch_Name Range Occurrences 1 G 2 5

2 D 2 23

3 F 2 22

4 C 2 57

5 A 2 20

6 A#/Bb 2 1 Total 128

Table 0.2: Table 1.2: ranking pitches based on occurrence No Pitch Name Range Occurrences Rank 1 G 2 5 5 2 D 2 23 2 3 F 2 22 3 4 C 2 57 1 5 A 2 20 4 6 A#/Bb 2 1 6 Total 128

Table 0.3: selecting top 5 first rate pitches

Top 5 Index Top

Pitch 5

Name notes

Index Index Range Rank Range Pitch set order 1 G 2 5 4 C 2 1 2 D 2 2 2 D 2 2 3 F 2 3 3 F 2 3 4 C 2 1 5 G 2 4 5 A 2 4 1 A 2 5

Table 0.4: revisit arrangement

PitchName Range Occurrences Rank Timing Pitch rank order Pitch order index 1 E 2 26 1 7.65095 C 1 2 Eb 2 19 2 3.36696 Eb 2 3 A 2 19 2 3.36109 E 3 4 Ab 2 13 4 1.38154 Ab 4 5 C 2/3 7/7 5/5 2.0317/ A 5 1.42718 Total 81

Table 0.5: measuring distance between well-adjusted pitches No

Major/

Minor Typical scale

Exact Range key

Pitch order rank Range Pitch order index Distance b/n pitches

1 C 2 1 ½ C or C2 Chernet 2 Eb 2 2 2 kinit(anchiho b b 3 E 2 3 ½ C2 E2 E2 A2 A2 ye lene) scale 4 Ab 2 4 1 ½ 5 A 2 5 1 ½

Appendix 4: data collection

Figure 0-11: letter of support

Figure 0-12: interviews with arch singer Getachew Birhanu

Figure 0-13: interview Questions with concerning body

Appendix 5: Sample Implementation Source Code

Figure 0-14: thesis implementation model code part-1

Figure 0-15: thesis implementation model code part-2

Figure 0-16: Loading Data and perform computations part-1

Figure 0-17: Loading Data and perform computations part-2