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A Comparative Study of Carnatic and Hindustani Raga Systems by Neural Network Approach

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A Comparative Study of Carnatic and Hindustani Raga Systems by Neural Network Approach International Journal of Neural Networks ISSN: 2249-2763 & E-ISSN: 2249-2771, Volume 2, Issue 1, 2012, pp.-35-38. Available online at http://www.bioinfopublication.org/jouarchive.php?opt=&jouid=BPJ0000238 A COMPARATIVE STUDY OF CARNATIC AND HINDUSTANI RAGA SYSTEMS BY NEURAL NETWORK APPROACH SRIMANI P.K.1 AND PARIMALA Y.G.2* 1Department of Computer Science and Maths, Bangalore-560 056, Karnataka, India. 2City Engineering College, VTU, Bangalore-560 062, Karnataka, India *Corresponding Author: Email- [email protected] Received: October 25, 2012; Accepted: November 06, 2012 Abstract- A unique Neural network approach has been used in the present investigations and a comparative study of the raga systems of Carnatic (CCM) and Hindustani classical music (HCM). The paper concerns a detailed study of the melakartha-janya raga system of CCM, Thaat-raaga system of HCM and cognitive studies of the same based on Artificial Neural networks (ANN). For CCM, studies were confined to the 72 melakartha ragas. For HCM 101 ragas were considered. Relative frequencies of notes in the scales were used as inputs. 100% accuracy was obtained for the melakartha system of CCM for several network topologies while highest accuracy was about 80% in case of HCM. Several networks, namely MLP, PCA, GFF, LR, RBF, TLRN were analyzed and consolidate report was generated. Keywords- Carnatic classical music, Hindustani classical music, thaats, melakartha ragas, cognition. Citation: Srimani P.K. and Parimala Y.G. (2012) A Comparative Study of Carnatic and Hindustani Raga Systems by Neural Network Ap- proach. International Journal of Neural Networks, ISSN: 2249-2763 & E-ISSN: 2249-2771, Volume 2, Issue 1, pp.-35-38. Copyright: Copyright©2012 Srimani P.K. and Parimala Y.G. This is an open-access article distributed under the terms of the Creative Com- mons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Introduction with variants- R, G, M, D, N. CCM defines 3 variants each for R, G, D and N and 2 for M (16 variations but 12 swarasthanas or semi- Indian Classical music consists of 2 forms- Hindustani and Carnatic tones due to overlap of 4 semitones) [Table-3]. music. CCM has its origin in Samaveda [17,18]. HCM is said to have come from Persia and adopted mostly in the Northern parts of In HCM, 2 variants are defined for each of the 5 notes, (12 semi- India [15,16]. HCM follows a 20-Thaat system [Table-1] while CCM tones). Semitones bear relative frequency ratio with reference note defines a 72-melakartha system [Table-2]. or adhara shruthi, S. Table 1- 10 THAATS of HCM [15,16] Ragas in CCM and HCM Systems Name of the Ragas are musical expressions which continually change with time. Notes present Carnatic equivalent That Ragas are inherently melodic and encompass other aspects such Bilawal S R G m P D N S-S N D P m G R S Dhira Sankarabharanam (29) as microtonal variations or gamakas. Ragas are formed by different Yaman S R G M P D N S-S N D P M G R S Mecha Kalyani (65) permutations of the notes. Such ragas are innumerable due to the Khamaj S R G m P D n S –S n D P m G R S Harikambhoji (28) immense possibilities of combinations of swaras. The raga classifi- Bhairav S r G m P d N S –S N d P m G r S Mayamalavagowla (15) cation and features in the 2 systems are compared in [Table-4]. Purvi S r G M P d N S –S N d P M G r S Kamavardhini (51) Artificial Neural Network (ANN) Marwa S r G M P D N S –S N D P M G r S Gamanashrama (53) A neural network is constructed by highly interconnected pro- Bhairavi S r g m P d n S –S n d P m g r S Hanuma Todi (08) cessing units (r neurons) which perform simple mathematical oper- Asavari S R g m P d n S-S n d P m g R S Natabhairavi (20) ations. A neural network can be trained to perform a particular Kafi S R g m P D n S –S n D P m g R S Kharaharapriya (22) function by adjusting the values of the connections (weights) be- Todi S r g M P d N S-S N d P M g r S Subhapantuvarali (45) tween elements, so that a given input results in a specific target output. The network is adjusted, based on a comparison of the Notes (Swaras), Semitones (Swarasthanas) in CCM and HCM output and target, until the network output matches the target [1-4]. Swaras are formed by the combination of fundamental frequencies ANN Approach to understanding Raga Classification in CCM and their overtones and are by themselves pleasing to the ears. 7 and HCM notes defined in both the forms are S,R,G,M,P,D,N,. They are clas- sified into Prakruti swaras-without variants- S and P; vikruti swaras- Based on the existing theories in both the forms of music we find International Journal of Neural Networks ISSN: 2249-2763 & E-ISSN: 2249-2771, Volume 2, Issue 1, 2012 || Bioinfo Publications || 35 A Comparative Study of Carnatic and Hindustani Raga Systems by Neural Network Approach that an extensive scientific process of classification method exists [13-15]. The present work is motivated by the interesting features in Carnatic music which has already been analyzed by the authors and results obtained in the previous works of the authors [5-15]. In using neural network models and very accurate results have been this work ANN approach has been used for the first time on HCM obtained using various network topologies, for 72 melakartha ragas system. A comparative analysis has been done using ANN for of CCM and also unique features of theses ragas due to tonic shift ragas of CCM and HCM systems. Table 2- 72 Melakarthas of CCM Showing The 12 Chakras, Melakartha Number, Name And Scale [17,18] ShuddhaMadhyamamela (M1) Variant notes Prathi Madhyama mela (M2) Mela No Chakra No /Name Melakartha Name R G D N Mela No Chakra No /Name Melakartha Name 1 1/ Kanakangi 1 1 1 1 37 7/ sAlaga 2 I Ratnangi 1 1 1 2 38 R jalArnava 3 N Ganamurthi 1 1 1 3 39 I JAlavarAli 4 D Vanaspathi 1 1 2 2 40 S navanIta 5 U Manavathi 1 1 2 3 41 H pAvani 6 Tanarupi 1 1 3 3 42 I Raghupriya 7 2/ Senavati 1 2 1 1 43 8/ gavAmbhodi 8 N Hanumathodi 1 2 1 2 44 V Bhavapriya 9 E Dhenuka 1 2 1 3 45 A shubhapantuvarAli 10 T Natakapriya 1 2 2 2 46 S shadvidhamArgini 11 R Kokilapriya 1 2 2 3 47 U suvarNAngi 12 A Rupavati 1 2 3 3 48 Divyamani 13 3/ Gayakapriya 1 3 1 1 49 9/B dhavalAmbari 14 A Vakulabharana 1 3 1 2 50 R nAmanArAyani 15 G Mayamalavagowla 1 3 1 3 51 A kAmavardhini 16 N Chakravaka 1 3 2 2 52 H Ramamanohari 17 I Suryakantha 1 3 2 3 53 M Gamanashrama 18 hATakambhari 1 3 3 3 54 A Vishvambari 19 4/ Jhenkaradhwani 2 2 1 1 55 10/ shyAmalAngi 20 V naTabhairavi 2 2 1 2 56 D Shanmukhapriya 21 E Keeravani 2 2 1 3 57 I simhEndramadhyama 22 D Kharaharapriya 2 2 2 2 58 S hEmavathi 23 A Gowrimanohari 2 2 2 3 59 H Dharmavathi 24 Varunapriya 2 2 3 3 60 I nIthimathi 25 5/ mAraranjini 2 3 1 1 61 11/ kAnthAmani 26 B chArukeshi 2 3 1 2 62 R Rishabhapriya 27 A sarasAngi 2 3 1 3 63 U lathAngi 28 N harikAmboji 2 3 2 2 64 D vAchaspati 29 A DhIrashankarabharan 2 3 2 3 65 R mEchakalyAni 30 nAganandini 2 3 3 3 66 A Chitrambari 31 6/ yAgapriya 3 3 1 1 67 12/A Sucharitra 32 R rAgavardhini 3 3 1 2 68 D jyotiswarUpini 33 I gAngeyabhushini 3 3 1 3 69 I dhAthuvardhini 34 T vAgadhiswari 3 3 2 2 70 T nAsikabhushini 35 U shUlini 3 3 2 3 71 Y kOsala 36 chalanAta 3 3 3 3 72 A Rasikapriya Table 3- Comparative Table Showing the Semitones in CCM and Data Sets HCM [15-18] The data sets for the present investigations consisted of the ragas Rel. freq. CCM-16 variants, 12 semitones HCM-12 semitones defined in the respective systems. In CCM only the melakartha ratio ragas were taken for input data sets. The frequencies of notes and Shadja-S 1 Shadj-S semitones of melakrtha ragas were determined from the relative Suddha Rishabha-R1 256/243 Komal Rishabh-r frequency ratios. Data sets consisted of 72 scales each with 7 at- Chatusruti Rishabha-R2/Shuddha gandhara-G1 09-Aug Suddh Rishabh-R tributes pertaining to the ascending of the scale. For HCM, 101 Shatshruthi Rishabha-R3/Sadharana Gandhara- 192/162 Komal Gandhar-g scales were used and normalised for equal number of attributes for G2 each scale. Attributes chosen were 10 each for ascending(aroha) Antara Gandhara-G3 81/64 Suddh Gandhar-G and descending (avaroha) and 20 for their combined analysis. Suddha Madhyama-M1 04-Mar Suddh Madhyam-m Prati Madhyama-M2 729/512 Teevra Madhyam-M Methodology Panchama-P 03-Feb Pancham-P Various ANN topologies were used for investigations- Multilayer Suddha Dhaivata-D1 128/81 Komal Dhaivat-d percepteron (MLP), Principle Component analysis (PCA), Radial Chatusruti Dhaivata-D2/Shuddha Nishadha-N1 27/16 Suddh Dhaivat-D Basis Function (RBF), Probabilistic Neural Network (PNN), Classifi- Shatshruthi Dhaivata-D3 /Kaisika Nishada-N2 16-Sep Komal Nishad-n cation Support Vector Machine (SVM), Generalized Feed forward Kakali Nishada-N3 243/128 Suddh Nishad-N (GFF), Time-delay (TDNN), Time-lag Recurrent (TLRN), Recurrent International Journal of Neural Networks ISSN: 2249-2763 & E-ISSN: 2249-2771, Volume 2, Issue 1, 2012 || Bioinfo Publications || 36 Srimani P.K.
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