Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

A COMPARATIVE ASSESSMENT OF THE DIFFERENT APPROACHES USED TO CLASSIFY THE MELODIC SCALES (RAAGS) IN HINDUSTANI

Dattatreya Mutalik Desai1, Sanjay H S2, Nandakishor Mutalik Desai3, Kirthana Kunikullaya4, Prithvi B S5 1,5 Medinxt Technologies Pvt Ltd, Bangalore, India 2,3Department of Medical Electronics Engineering, M S Ramaiah Institute of Technology, Bangalore, India 4 Department of Physiology, M S Ramaiah Medical College, Bangalore, India email: [email protected]

ABSTRACT

Hindustani Indian Classical Music (ICM) is one of the oldest and ancient forms of Indian music culturewhich is existing and is largely being followed and performed even at present in different parts of the country, although Carnatic form of ICM is more predominant in Southern states of India. Music being a qualitative phenomenon, seldom do researchers assess the same with a quantified approach. The present article highlights one such quantitative approach being followed so as to classify a given Hindustani ICM Raaga with the aid of different approaches. In order to classify the raaga, attributes such as , Arohan, Avarohan, Pakkad, Raagang have been used. While Raagang based approach posed difficulties pertaining to the uncertainties in Pakkad, Arohan and Avarohan based approaches have been found to provide an accuracy of about 85% for and , but an accuracy of 70.18% for due to the incorporation of Meend (Flow from one note to another without pause) and the Chikari (Note used in between two phrases)Sa in the Raags. However, it was to be noted that the Thaat based classification provided an accuracy of 86% for every Raaga and hence was concluded to be the best approach for automated classification of Raagas. Incorporation of digital approaches aided in the ease of the experimental paradigms and hence were preferred over conventional analog approaches. Such approaches can help to develop artificial intelligence-based methods to help in the classification as well as the presentation of Raagas in the near future with a higher accuracy. Also, a combination of different attributes could be explored to develop better classification systems as a next step in this work.

Keywords: Hindustani Indian Classical Music; Carnatic Indian Classical Music; Raaga; Thaat; Raagang; Arohan and Avarohan; Artificial intelligence

I. INTRODUCTION Indian Classical Music (ICM) is perhaps one of the oldest forms of music known and termed so due to its Indian origin. It is well quoted in different Vedic chants, songs and other music-based domains in ancienthistory [1]. Indian music is known to be melody oriented and is further classified into Hindustani music (originated in the northern part of India) and (originated in the southern part of India) [2]. While Hindustani music is known to depend on the improvisation on raaga-based aspects, Carnatic equivalent depends on the composition-based improvisation. However, both these sects of music depict numerous commonalities and very few differences. Abundant information about ICM is available in Hindu Vedic texts. However, the best source that one could quote would be the Sanskrit text “Sangeetha Ratnakara” written by “Saranga ”, according to which ICM is based on two basic entities namely Raaga (melodic scale) and [3]. The ICM depends on a 12- note-scale with 7 natural notes called the ShudhSwars(r,g,m,d,n) and 5 derived half-notes called the Komal swaras (r,g,d,n) (Also called as Flat Note in western system) and the swara(m) (Also called as Sharp note in Western system) [9]. As compared to its western counterpart, for the base note termed as Sa frequency remains fixed (also called as PrakruthiSwar) while in the Vikruthaswara (the rest of the notes in the scale) frequency changes as per the chosen and composition of its notes.

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Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

Swar, is a term in Sanskrit which is known to connect onto an octave. It provides a comprehensive dimension to the scale of a given raag [14]. Swar can be divided into two types, AchalaSwar (PrakruthiSwar) and ChalaSwar (Vikruthi Swar). AchalaSwar refers to the fixed notes namely Sa and Pa, astheir frequencies are fixed for a given Shruthi (Pitch) [15], the ChalaSwar have different frequencies for the given Shruthi (Re, Ga, Ma, Dha and Ni) The different frequencies are labelled as Komal (Frequency lesser than specified standard frequency), Shudh (Specified Standard frequency) and Teevra (Frequency greater than Specified standard frequency), which are selected based on a specific Raag. Swar Re, Ga, Dha, Ni can be represented in Shudh and Komal while Ma can be presented in Shudh and Teevra according to Indian Standard Music Scale.

Raaga is known to be dependent on Swaras (notes) thereby forming the melody-based aspects while the Taal is relative to the timeline and hence aids to determine the tempo & rhythm of composition to the raag [4]. Due to the fact that the different patterns of notes and associated rhythm of their rendition creates the unique melody, Hindustani as well as the Carnatic music is often perceived to be different, even though they are similar in terms of their origin [5].

The word Raaga is adapted from the Sanskrit word “Ranj” and is known to have a positive impact on the feeling of the listeners [6]. It is based on swaras and is known to depict a definite set of form, structure as well as the sequence so as to provide a mood as well as a definite meaning to the raag. For instance, each of the Graha Swara, (often known as the initial note), the Amsha Swara (the expressive note within a raaga) and the Nyasa Swara (the final note) have different and unique significance with regard to the composition of the Raaga. Every Raaga is known to incorporate two aspects namely the Arohan (ascending notes) and the Avarohan (descending notes) as its basic structure [8].

Raag is a unique but a definitive framework of a predefined set of Swars developed so as to provide a particular bhaava (Feelings/ emotions/ rasas) [10]. Raag is the epicenter of music [11]. Unlike Western music, raag improvisation has no script in ICM and is based on Arohan and Avarohan of a given Raag which may be Sampurna, Audhava or Shadhava [12].Sampurna are those which have all 7 notes in the ascending and descending order. Shadhava is composed of 6 Swars in Arohan and Avarohan each. Audhava is made up of Arohan and Avarohan, each of 5 Swars. Vakra is made up of different numbers of notes with addition or deletion of 1 or 2 notes or jumbling of the notes. There are more than about 10000 raags because of permutation and combinations of notes in different orders. Each raag is given a unique name, for example, Bhoop, , , , Rageshri, , , Basant Mukhari etc. When an individual chooses a raag for concert (performance), numerous aspects have to be catered to, such as those pertaining to the timing, mood etc. based on which appropriate improvisation is seen in the raag being played.

Raags are known to invoke and stimulate the human body and also vary the psychological status of an individual. Raags produce Rasas (Moods) known as the Nava Rasas namely Shringara (love/beauty), Hasya (laughter), Karuna (sorrow), Raudra (anger), Veera (heroism/courage), Bhayanaka (terror/fear), Bibhatsya (disgust), Adbutha (surprise/wonder), and Shantha (peace or tranquility). Hindu Shastras also mention different raags for different seasons and variations in the same. For instance, raagBhairavi is known to create sad/ sorrow mood[13].

Raags are based on ten namely , Bilawal, , , Khamaaj, , , , Marwa and . (Thaats are the set of scales based on which the Raags are derived). There have been various predefined rules pertaining to Raags. The Swars are often fixed in the Raag. While improvising the notes are to be used in a predefined manner so as to ornament them and emphasize or deemphasize them accordingly. There is also a specific approach to scale the Arohan as well as the Avarohan. Raags could be improvised by the means of Pakkad (important phases). Very few Raags such as those of Lalat, have Komal/TeevraSwar (half note) followed by Shudhswar (full note), which is otherwise not permissible in Hindustani style of music. The performance as well as the time (Prahar) of a given raaga has a definite impact upon the emotional state of the listener as well as the performer.

Swar, is a term in Sanskrit which is known to connect onto an octave. It provides a comprehensive dimension to the scale of a given raag [14]. Swar can be divided into two types, AchalaSwar (PrakruthiSwar) and ChalaSwar (Vikruthi Swar). AchalaSwar refers to the fixed notes namely Sa and Pa, astheir frequencies are fixed for a given Shruthi (Pitch) [15], the ChalaSwar have different frequencies for the given Shruthi (Re, Ga, Ma, Dha and Ni)The different frequencies are labelled as Komal (Frequency lesser than the standard frequency), Shudh (Standard frequency) and Teevra (Frequency greater than standard frequency), which are selected based on a www.turkjphysiotherrehabil.org 5197

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X specific Raag. Swar Re, Ga, Dha, Ni can be represented in Shudh and Komal while Ma can be presented in Shudh and Teevra according to Indian Standard Music Scale.

Need for the present review Considering all the facts such as Arohan, Avarohan, Raagang, and Thaat systems for the classifications of the raags, it is required to find out and review the best methods for the classification of raags of ICM.

Materials and Methods This section highlights three different approaches so as to classify a given raag based on Thaat method, Raagang method and Arohan&Avarohan approach.

Thaat method of classification A Thaat is a parent scale and hence forms a definitive basis for the categorization of a given Raag. Thaats are known to be Septatonic in nature. However, a raag may not align itself to every note of its parent scale i.e., the Thaat. Thaat incorporates only the Arohan (ascending order) of all the notes of the Sapthaka (Octave) which includes Shudh, Komal and TeevraSwars in different combinations. Overall, there are ten Thaats and every Raag can be classified into any of these Thaats [Table 1].

Table 1: Different Thaats and their corresponding Swars Sl No Name of Thaats Swars 1 Bilawal S R G m P D N 2 S R G m P D n 3 Kafi S R g m P D n 4 Asavari S R g m P d n 5 Bhairavi S r g m P d n 6 Bhairav S r G m P d N 7 Kalyan S R G M^ P D N 8 Marwa S r G M^ P D N 9 Poorvi S r G M^ P d N 10 Todi S r g M^ P d N Note: - S denotes Sa, r denotes the Komal Re, R denotes Shudh Re, g represents Komal Ga, G represents Shudh Ga, M denotes Shudh Ma, M^ denotes Teevra Ma, P denotes Pa, d denotes Komal Dha, D denotes Shudh Dha, n denotes Komal Ni and N denotes Shudh Ni. The red color indicates the Vikrutha Swars

A professional musician can easily identify the thaatof a Raag by giving it a simple audition. The important aspect in such classification is to identify the notes and their associated combinations used in the Raag. Then these notes are used to recognize the presence of the key note. The important feature so as to assess and classify a given Raag into a definite Thaat is based on the combination of Komal, Shudh and TeevraSwars. In convention, the classification could be achieved in the following combinations;

• If there are no Komal notes then the Raag is from Bilawal Thaat.

• If there is one Komal note, and

• If Ma is Teevra then it is classified under Kalyan Thaat.

• If Ma is not Teevra then it is recognized as Khamaj Thaat.

• If there are two Komal notes and then with

• Komal Re &Komal Dha, then the Raag is of Bhairav Thaat.

• Komal Ga &Komal Ni, then the Raag is of Kafi Thaat.

• Komal Re &Teevra Ma, then the Raag is of Marwa Thaat

• If there are three Komal notes, and then with www.turkjphysiotherrehabil.org 5198

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

• Komal Ga, Komal Dha and Komal Ni, then the Raag is of Asavari Thaat.

• Komal Re and Komal Dha and Teevra Ma, the Raag is of Poorvi Thaat.

• Komal Re, Komal Ga and Komal Dha and Teevra, the Raag is of Todi Thaat.

• If there are four Komal notes, then the Raag is of Bhairavi Thaat.

In one of the previous studies the objective was to classify the predefined 3 raags with ten thaats resulting in an overall set of 30 raags for testing. The Raags were recorded at a sampling rate of 44,100 samples per second in a 16-bit mono channel with a base note Sa at 260 Hz in the middle octave. The database contained 12 notes in lower, middle as well as the higher octaves. Each sample was further divided into a frame of 100 ms each with the aid of cepstral technique. The statistical aspects of the same were found along with the fundamental frequency. The frequency range of the middle octave was calculated with the aid of a Chebyshev inequality filter and this information was further utilized so as to arrive at the counterparts of the lower as well as the higher octaves by multiplying and dividing the middle octave frequency range by 2 accordingly. The input audio was converted into frames of 100 ms each using a hamming window. The fundamental frequency of the sample data was calculated using cepstral analysis. This value was compared against the frequency ranges of the database so as to compare and arrive at the note and the octave of the sample audio bit. The reference values were that of Komal and TeevraSwar used in the Raags. A pictorial representation of the workflow of this approach is provided in figure 1 (1a – Database preparation, 1b – Audio processing, 1c – Thaat identification) [16].

Figure 1: Thaat based classification – on overview

(Figure 1a) : Creation of the database

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(Figure 1b) : Audio processing

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Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

(Figure 1c) : Thaat identification

Y – Yes , N - Number Raagang method of classification It was previously shown that every raag has a Pakkad (Raagang) of its own. Pakkad is nothing but a cluster of notes containing only the notes present in raag and combined with the rules of the movement of Swars. These phrases are played/performed frequently in the presentation. So, anyone who identifies these phrases can easily identify the Raag. Raagang, known more commonly as the Pakkad of a given Raag are initially digitized using suitable software for automated classification tasks. Thaat as well as the Pakkad could be pre-recorded and fed into the software so as to provide a template-based matching which could help in the classification of a given Raag for those with the best match. A few Raags and their Pakkads are provided in Table 2 for a better understanding of the classification process

Table 2: Raags and their Pakkads Sl No Raag Pakkad 1 Bhairav G m r S S G m P G m d P 2 Bhoop P R G G R D D S 3 Todi M^ g r g r S S r g M^ d 4 Pooriya M G N r S 5 Hamsadhwani G R N P S 6 Bageshree m P D g R 7 Yaman N R G M^ G G R N D N R 8 Lalat N r G m M^ m 9 Kafi M P g R 10 Marwa N r G M^ D D M^ G r Note: The symbols as explained in table 1note.the blue colored box in table 2 indicates the gap between two Pakkads, thegray colored box in table 2 indicates the empty space. Certain raagas can be identified by a single Pakkads while the rest, by combination of multiple pakkads. On similar lines, every raag can be classified based on their respective ragang (pakkads) using digital template matching based approaches. In the present study, the pakkad was recorded, digitized and stored in a memory device. This was considered as the database for the classification. The sample audio bit to be classified was compared and assessed for a possible match in the database. The template matching technique was used here to find the match between the pakkad and the input audio. The matched bit corresponded to the respective Raag. This process is depicted in [17] Figure 2.

Figure 2: Raagang based classification of Raags

Arohan and Avarohan Method of classification A high degree of variability in the frequency of the base note (Sa) is acceptable in case of the ICM. However, the Aadhaar Shadj (base swar) itself is not fixed and is often seen to be varying from its original frequency value. A www.turkjphysiotherrehabil.org 5201

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X reference swar, with a predefined frequency is used to assess the frequencies of the other swars by normalizing the tempered scale in this approach. Although such an approach does not present a comprehensive assessment of a given raag, it ascertains a given raag as a sequence of predefined swars. Then, the frequencies of the notes whose raag is to be assessed, is analyzed for its frequencies. This information is later compared with the standard frequency value and hence the raag is identified. In essence, Indian Swar is quantified with an equivalent Western note and then, a comparison is drawn. A sample comparison is provided in Table 3.

Table 3: Indian Swars and their western equivalent frequencies Octave MadhyaSaptak Swar Mandra Saptak (3rd Octave) Taar Saptak (5th Octave) (4th Octave) Western J Name Freq Name Freq Name Freq S C 3 S 131 4 S 262 5 S 523 r CA / Db 3 r 139 4_r 277 5 r 554 R D 3 R 147 4 R 294 5 R 587 g DC / Eb 3 156 4 311 5 622 G E 3_G 165 4 G 330 5_G 659 m F 3 M 175 4_M 349 5 M 698 M^ FT / Gb 3_M^ 185 4 M^ 370 5_M^ 740 P G 3 P 196 4_P 392 5 P 784 d G#/Ab 3 d 208 4 d 415 5 d 831 D A 3_D 220 4_D 440 5_D 880 n A#/Bb 3 n 233 4 n 466 5 n 932 N B 3 N 247 4 N 494 5 N 988 [Note : - b – Flat Note , # - Sharp Note]

In another study, a database of Arohan and Avarohan of 18 raags (these raags were selected upon the basis of stratified random sampling) was initially recorded using 3 different instruments (Santoor, Sarod, Sitar – Table 4) with a sampling rate at 44100 Hz. The base frequency or AadharShadj was tuned to 262Hz. The input audio to be classified was a simple continuous time audio signal, which was further divided into the corresponding frames of 371 ms using hanning window. Later The fundamental frequency of each of these frames were obtained using zero crossing and autocorrelation techniques. Then, with the aid of the harmonic product spectrum, the audio bit was down-sampled by 2 and then again by 5. Both these outputs were then used to compare the bits with the database and thus the notes of the audio bits were identified. Then Arohanand Avarohan were compared with the stored database and finally the Raag of the input audio was found. This process if pictorially depicted in figure 3[18].

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Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

Figure 3: Arohan and Avarohan based approach

Discussion and concluding remarks Various researchers have been using the aforementioned approaches to classify a given Raag.

The thaat identification involved counting the number of Vikruthaswars. Based on the number of Vikruthaswars and the nature of the Vikruthaswars detected, the thaat of the raag is recognized. The key feature here was the accuracy of the detection of the swar, achieved based on cepstral analysis. This was because of the fact that the cepstral analysis is known to be useful to assess the fundamental frequency of the vocal cord and the music signal amidst background noises. An overall accuracy of 86% was achieved using this technique.

Yet another research, was successful in the development of a digital approach so as to classify a given Raag based on the Raagang approach with an accuracy of 78%. Another novel aspect of this research was the development of an automated approach for this classification. Direct application of Shruthi and Swar could be incorporated with the elaboration of the Raagang. Thaat with Raagang based classification would however yield better results and hence could be probed into, as the next step, as per the literature.

With the incorporation of arohan and avarohan approach for classification, an automated approach was developed to classify a given raaga. A predefined sequence of swars were analyzed in this method for the identification of raags. The arohan and avarohan patterns, being well defined for each of the Raags, became the basis for this approach, which were recorded on a monophonic setup. Three string instruments were played namely Santoor, Sarod and Sitar. An accuracy of 85% was obtained in the classification of the Raags for Santoor and Sarod but was 70.18% for Sitar due to the incorporation of the Meend and the Chikari Sa during the recording of the Raag. However, the accuracy could be improved with the inclusion of the process of Swar onset detection process. Also, inclusion of additional attributes like Pakkad, along with the conventional Arohan and Avarohan could be beneficial for the classification process.

Hindustani Raags can be successfully classified into various Thaats by the detection of key derived Swars namely those of Komal and TeevraSwars used in the Raags. The accuracy of the process depends on the accuracy of swar detection and the key swars that must be present in the performance. Although there have been some experiments performed based on the Raagang (Pakkad), there seems to be much work left to be done in this approach. The Arohan and Avarohan approach is well documented and hence preferred for the identification of a given Raags. Monophonic recording is sufficient in such approaches. Santoor and Sarod based Raags can be classified better than those played with Sitar. www.turkjphysiotherrehabil.org 5203

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

Based on the existing literature, it is evident that the Thaat based classification is the more reliable because the identification and classification of Raags in Hindustani ICM encompasses certain constraints such as those of the presence of similar Raags with different Pakkad, similar Swars with varied importance for each of the similar Swars and also due to the presence of certain Non-Hindustani raags which are known to be borrowed from Carnatic ICM system. Another factor impairing the classification process is the difference in the approach and the presentation of the Raags by different artists which is never the same. Although Arohan and Avarohan based approach seems to be reliable, it is Thaat based classification which is the best due to a higher level of accuracy and reliability for the classification-based applications. Also, the Thaat based approach can be used successfully to classify the Non-Hindustani Raags from other systems of music as well. Hence it is best inferred that Thaat based classification is more suitable than the other two approaches reviewed in this article. Digital classification aids in the development of computer-based approaches for better classification of Raags as well. However, one needs to always emphasize on the fact that Raags are qualitative aspects rather than quantitative factors, and hence there will always be challenges encountered while developing automated machine learning based approaches for the classification of Raags of any kind and these would have to be addressed on a case-to-case basis as and when such problems arise. Finally, any future usage of technology or artificial intelligence would aid to address the issue of Raag identification and make it easier for general connoisseurs and artists.

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