Swara Histogram Based Structural Analysis and Identification of Indian Classical Ragas
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SWARA HISTOGRAM BASED STRUCTURAL ANALYSIS AND IDENTIFICATION OF INDIAN CLASSICAL RAGAS Pranay Dighe1, Harish Karnick1, Bhiksha Raj2 1. Indian Institute of Technology, Kanpur, India. 2. Carnegie Mellon University, Pittsburgh PA, USA [email protected], [email protected], [email protected] ABSTRACT has layers of additional finer structure that can help in con- tent identification and classification, yet these aspects have This work is an attempt towards robust automated analysis been under-utilised by the music analysis community. of Indian classical ragas through machine learning and sig- While an expert in Indian Classical music can identify a nal processing tools and techniques. Indian classical mu- raga just by noticing the unique constituent patterns such as sic has a definite heirarchical structure where macro level swaras (notes), arohan, avarohan and pakad in the perfor- concepts like thaats and raga are defined in terms of mi- mance (explained later), developing computational mod- cro entities like swaras and shrutis. Swaras or notes in els for the same has been a challenging task for music re- Indian music are defined only in terms of their relation to searchers. The freedom that Indian classical music pro- one another (akin to the movable do-re-mi-fa system), and vides to an artist to give his/her own personal flavour to a an inference must be made from patterns of sounds, rather raga makes it harder for a novice to identify two different than their absolute frequency structure. We have devel- performances of the same raga. Another key challenge is oped methods to perform scale-independent raga identifi- the fact that swaras are defined only in terms of their rela- cation using a random forest classifier on swara histograms tion to one another, and inferences must be made from pat- and achieved state-of-the-art results for the same. The ap- terns of sounds, rather than their absolute frequency struc- proach is robust as it directly works on partly noisy raga ture. This motivated us to move away from diretly trying recordings from Youtube videos without knowledge of the to identify notes as features and conceptualize various fea- scale used, whereas previous work in this direction often tures based on swaras and their structural form. use audios generated in a controlled environment with the desired scale. The current work demonstrates the approach for 8 ragas namely Darbari, Khamaj, Malhar, Sohini, Ba- 2. APPLICATIONS har, Basant, Bhairavi and Yaman and we have achieved Automatic Tagging/Annotation: Continued digitisation an average identification accuracy of 94:28% through the of old archives of classical music and the sole availabil- framework. ity of newer classical performances in digital form has re- sulted in huge digital databases of music. Automatic con- 1. INTRODUCTION tent tagging of this unorganised media is important to gen- Music information retrieval(MIR) is an active and grow- erate metadata for available data and thus facilitate creation ing field of research as most music today whether available of easily accessible databases. Bertin-Mehiuex et al. have over the internet or otherwise is in digital form. The im- already created a tool [7] for music tagging, but the work portant feature of classical music, both western and Indian, does not include Indian ragas. that distinguishes it from other kinds of music is that it is Music Recommendation System: Based on an user’s ear- supported by a proper well established theory and rules. lier choices of music, a music recommendation system fil- There has been a lot of work on content analysis of west- ters out and recommends music through tags or content ern classical music in terms of information retrieval, genre analysis. In the Indian system ragas are associated with detection and instrument/singer identification etc. for ex- emotions, time of the day, and seasons of the year. For ample (see [1], [3], [2]). Though Indian Classical music queries based on such criteria, a proper recommendation is also a major form of music, the current literature related system can make appropriate choices using raga identifi- to it is very limited, in comparison to its western coun- cation as a base. terpart. Indian classical music is known for its technical Music Tutoring/Correctness Detection System: A prob- soundness and its well defined structure. A basic musi- abilistic raga identification system can be modified to a tu- cal performance unit, akin to a song, is a raga.A raga toring [9] or correctness detection system as well. While achieving this for a complex professional musical perfor- Permission to make digital or hard copies of all or part of this work for mance might be difficult, yet using it for cleaner and sim- personal or classroom use is granted without fee provided that copies are pler instrumental versions of a raga may give positive re- not made or distributed for profit or commercial advantage and that copies sults. Mistakes like skipping some swaras or a particular bear this notice and the full citation on the first page. rule not being followed, can be identified by such a tutor- c 2013 International Society for Music Information Retrieval. ing system. Raga Generation: Within some broad rules, Indian clas- and perform random forest classifier based classification to sical music allows a performer to modify the components achieve state-of-the-art results. of ragas according to his creativity to create his own per- sonalised performance of that raga. Generative models in- 4. INDIAN CLASSICAL MUSIC: BASIC duced from human raga performances can be used to syn- STRUCTURAL ELEMENTS thesize new raga performances that can be made unique or personalized by injecting controlled randomness or varia- The theory of Indian classical music discussed in this chap- tions. ter is based on the texts [8] of well known Indian musicol- ogist and scholar Vishnu Narayan Bhatkhande. We first 3. RELATED WORK discuss some important concepts of Indian classical mu- sic that are needed to understand the methods used in this For a novice human ear, the basic way of recognising a raga work. is to correlate two tunes on the basis of how similar they sound. A trained expert on the other hand looks for char- 4.1 Swaras acteristic phrases like arohanam, avrohanam, pakad and constituent swaras, gamakas etc. to arrive at a conclusion Swaras (or notes) correspond to the frequencies being per- about the raga. This well defined manner of raga identifica- formed vocally or by a musical instrument. Collectively tion using the above properties has motivated researchers the seven Swaras are symbols used for a set of frequency to conceptualize computational models for them. values. They are the constituent units of a raga and thus act Sahasrabuddhe and Upadhye [10] (1992) modelled a as an alphabet. They are closely related to the solfege (do raga as a finite state automaton based on the swara con- re mi fa so la ti) in western music. Basically, there are 7 stituent sequential patterns. Pandey et al. (2003) [11] ex- swaras in Indian classical music: Shadja (Sa), Rishab (Re), tended this idea of swara sequence in the “Tansen” raga Gandhara (Ga), Madhyama (Ma), Panchama (Pa), Dhai- recognition system where they worked with Hidden Markov vata (Dh), and Nishad (Ni). These swaras are related to Models on swara sequences. These swara sequences were each other by the fixed ratio of absolute frequency values extracted using two methods- hill peak heuristic and note they denote. Similar to notes in western music, we get the duration heuristic. They also employed two separate pakad same swara one octave above or below a particular swara. matching algorithms that improved the HMM based re- The notes Sa, Re, Ga, Ma, Pa, Dha and Ni are today sults. The first algorithm used substring matching for pakad known as ”shuddha svaras” or ”pure notes”. This expres- identification and the second algorithm was based on count- sion is used in contrast to ”vikrta svaras” or ”altered notes”. ing occurences of n-grams of frequencies in the pakad. Thus Re may be flattened to get the vikrta swara ”Komal Tansen was able to perform with an accuracy of 87% on Re” and Ma can be ”sharpened” to get the vikrata swara a dataset of two ragas. ”tivra” Ma. Sa and Pa only have the Shuddha(pure) form, In [13] Sreedhar and Geetha created a database of ragas while the rest have variants in Tivra(sharp) or Komal(soft) and used the scale of the raga performance as the similarity forms. Table 1 describes relations between the various metric. Within a scale, notes are matched with the exist- swaras and similarity to western notes if C is labelled as ing sets of notes in the ragas in the database. The closest Sa. raga in the database is given as the output for the test raga. Chrodia and Rae [12](2007) derived Pitch-class Distribu- Table 1. Scale of 12 Swaras used in Ragas (if the note C is tions (PCD) and Pitch-class Dyad Distributions (PCDD) labelled as the swara Sa) Hindustani Name (Symbol) Solfa Scale of C Ratio to Sa from Harmonic Pitch Class Profiles (HPCP) and used these Shadja (Sa) Doh C 1 distributions for classification using SVMs. They achieved Komal Rishabh (Re) C#,Db 256/243 state-of-the-art results with accuracies of 78% for PCDs Shuddha Rishabh (Re) Re D 9/8 Komal Gandhr (Ga) D#,Eb 32/27 and 97.1% for PCDDs. The dataset they had used con- Shuddha Gandhr (Ga) Mi E 5/4 sisted of 17 ragas played by a single artist. Shuddha Madhyam (Ma) Fa F 4/3 Inspired by the use of HMMs over swara sequences and Tvra Madhyam (M) F#,Gb 45/32 Pancham (Pa) Sol G 3/2 PCDs and PCDDs in [11] and [12], we propose a new ap- Komal Dhaivat (Dha) G#,Ab 128/81 proach to raga identification using swara based features ex- Shuddha Dhaivat (Dha) La A 5/3 tracted from chromagrams.