Abstract
Technological advances have resulted in massive digital music growth and created the need to efficiently analyze large volumes of digital music and extract helpful information needed to perform various musical tasks. Classical music in the Indian subcontinent, known as Indian classical music (ICM), has a long tradition and many followers. ICM has always been less researched, but it has changed in the past decade. Many researchers have started focusing on the tasks of ICM. These analyses and approaches by various researchers must be combined and analyzed in-depth to develop future research avenues. Therefore, this chapter critically reviews various approaches for the fundamental tasks in ICM. The basic concepts of ICM are also described in detail to get a precise hold of musical ideas. Moreover, the signal processing methods are examined to draw out valuable characteristics for specific tasks and their strengths and shortcomings in ICM. This chapter also highlights some broad research problems with the present methodologies and potential solutions to correct and increase efficiency.
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References
Alvarez, A.A., Gómez, F.: Motivic pattern classification of music audio signals combining residual and lstm networks. International Journal of Interactive Multimedia & Artificial Intelligence 6(6) (2021)
Belle, S., Joshi, R., Rao, P.: Raga identification by using swara intonation. Journal of ITC Sangeet Research Academy 23(3) (2009)
Bellur, A., Ishwar, V., Murthy, H.A.: Motivic analysis and its relevance to raga identification in carnatic music. In: Serra X, Rao P, Murthy H, Bozkurt B, editors. Proceedings of the 2nd CompMusic Workshop; 2012 Jul 12–13; Istanbul, Turkey. Barcelona: Universitat Pompeu Fabra; 2012. p. 153–157. Universitat Pompeu Fabra (2012)
Bellur, A., Ishwar, V., Serra, X., Murthy, H.A.: A knowledge based signal processing approach to tonic identification in indian classical music. In: Serra X, Rao P, Murthy H, Bozkurt B, editors. Proceedings of the 2nd CompMusic Workshop; 2012 Jul 12–13; Istanbul, Turkey. Barcelona: Universitat Pompeu Fabra; 2012. p. 113–118. Universitat Pompeu Fabra (2012)
Boersma, P.: Praat, a system for doing phonetics by computer. Glot. Int. 5(9), 341–345 (2001)
Boersma, P., et al.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: Proceedings of the institute of phonetic sciences. vol. 17, pp. 97–110. Citeseer (1993)
Bozkurt, B., Srinivasamurthy, A., Gulati, S., Serra, X.: Saraga: research datasets of indian art music (May 2018). https://doi.org/10.5281/zenodo.4301737
Bozkurt, B., Karaosmanoğlu, M.K., Karaçalı, B., Ünal, E.: Usul and makam driven automatic melodic segmentation for turkish music. Journal of New Music Research 43(4), 375–389 (2014)
Camacho, A.: SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. University of Florida Gainesville (2007)
Cambouropoulos, E.: Musical parallelism and melodic segmentation:: A computational approach. Music Perception 23(3), 249–268 (2006)
Chakraborty, S., De, D.: Object oriented classification and pattern recognition of indian classical ragas. In: 2012 1st International Conference on Recent Advances in Information Technology (RAIT). pp. 505–510. IEEE (2012)
Chapparband, M., Kulkarni, M.G., Sameeksha, D., Krishna, A.V., Bhat, A.: Shruti detection using machine learning and sargam identification for instrumental audio. In: Advances in Speech and Music Technology, pp. 145–156. Springer (2021)
Chordia, P., Rae, A.: Raag recognition using pitch-class and pitch-class dyad distributions. In: ISMIR. pp. 431–436. Citeseer (2007)
Chordia, P., Şentürk, S.: Joint recognition of raag and tonic in north indian music. Computer Music Journal 37(3), 82–98 (2013)
Chowdhuri, S.: Phononet: multi-stage deep neural networks for raga identification in hindustani classical music. In: Proceedings of the 2019 on international conference on multimedia retrieval. pp. 197–201 (2019)
Datta, A.: Generation of musical notations from song using state-phase for pitch detection algorithm. J Acoust Soc India XXIV (1996)
Datta, A., Sengupta, R., Dey, N., Nag, D.: A methodology for automatic extraction of meend from the performances in hindustani vocal music. Journal of ITC Sangeet Research Academy 21, 24–31 (2007)
De Cheveigné, A., Kawahara, H.: Yin, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America 111(4), 1917–1930 (2002)
Dighe, P., Agrawal, P., Karnick, H., Thota, S., Raj, B.: Scale independent raga identification using chromagram patterns and swara based features. In: 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). pp. 1–4. IEEE (2013)
Dighe, P., Karnick, H., Raj, B.: Swara histogram based structural analysis and identification of indian classical ragas. In: ISMIR. pp. 35–40 (2013)
Dutta, S., Murthy, H.A.: Discovering typical motifs of a raga from one-liners of songs in carnatic music. In: ISMIR. pp. 397–402 (2014)
Dutta, S., Murthy, H.A.: A modified rough longest common subsequence algorithm for motif spotting in an alapana of carnatic music. In: 2014 Twentieth National Conference on Communications (NCC). pp. 1–6. IEEE (2014)
Dutta, S., PV, K.S., Murthy, H.A.: Raga verification in carnatic music using longest common segment set. In: ISMIR. vol. 1, pp. 605–611. Malaga, Spain (2015)
Foundation, M.: Musicbrainz - the open music encyclopedia. https://musicbrainz.org/, (Accessed on 07/14/2021)
Gaikwad, C.J., Kodag, R.B., Patil, M.D.: Tonic note extraction in indian music using hps and pole focussing technique. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). pp. 1–5 (2019). https://doi.org/10.1109/ICCCNT45670.2019.8944486
Ganguli, K.K., Lele, A., Pinjani, S., Rao, P., Srinivasamurthy, A., Gulati, S.: Melodic shape stylization for robust and efficient motif detection in hindustani vocal music. In: 2017 Twenty-third National Conference on Communications (NCC). pp. 1–6 (2017). https://doi.org/10.1109/NCC.2017.8077055
Ganguli, K.K., Rao, P.: A study of variability in raga motifs in performance contexts. Journal of New Music Research 50(1), 102–116 (2021)
Ganguli, K.K., Rastogi, A., Pandit, V., Kantan, P., Rao, P.: Efficient melodic query based audio search for hindustani vocal compositions. In: ISMIR. pp. 591–597 (2015)
Gawande, K.: Raga recognition in indian classical music using deep learning. In: Artificial Intelligence in Music, Sound, Art and Design: 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings. vol. 12693, p. 248. Springer Nature (2020)
Gulati, S., Bellur, A., Salamon, J., HG, R., Ishwar, V., Murthy, H.A., Serra, X.: Automatic tonic identification in indian art music: approaches and evaluation. Journal of New Music Research 43(1), 53–71 (2014)
Gulati, S., Salamon, J., Serra, X.: A two-stage approach for tonic identification in indian art music. In: Proceedings of the 2nd CompMusic Workshop; 2012 Jul 12–13; Istanbul, Turkey. Barcelona: Universitat Pompeu Fabra; 2012. p. 119–127. Universitat Pompeu Fabra (2012)
Gulati, S., Serrà Julià, J., Ganguli, K.K., Sentürk, S., Serra, X.: Time-delayed melody surfaces for rāga recognition. In: Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7–11; New York City (NY).[Canada]: ISMIR; 2016. p. 751–7. International Society for Music Information Retrieval (ISMIR) (2016)
Gulati, S., Serrà Julià, J., Ganguli, K.K., Serra, X.: Landmark detection in hindustani music melodies. In: Georgaki A, Kouroupetroglou G, eds. Proceedings of the 2014 International Computer Music Conference, ICMC/SMC; 2014 Sept 14–20; Athens, Greece.[Michigan]: Michigan Publishing; 2014. Michigan Publishing (2014)
Gulati, S., et al.: Computational approaches for melodic description in indian art music corpora. Ph.D. thesis, Universitat Pompeu Fabra (2017)
Gupta, C., Rao, P.: Objective assessment of ornamentation in indian classical singing. In: Speech, Sound and Music Processing: Embracing Research in India, pp. 1–25. Springer (2011)
Huang, X., Acero, A., Hon, H.W., Reddy, R.: Spoken language processing: A guide to theory, algorithm, and system development. Prentice hall PTR (2001)
Ishwar, V., Dutta, S., Bellur, A., Murthy, H.A.: Motif spotting in an alapana in carnatic music. In: ISMIR. pp. 499–504. Citeseer (2013)
John, S., Sinith, M., Sudheesh, R., Lalu, P.: Classification of indian classical carnatic music based on raga using deep learning. In: 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS). pp. 110–113. IEEE (2020)
Karakurt, A., Şentürk, S., Serra, X.: Morty: A toolbox for mode recognition and tonic identification. In: Proceedings of the 3rd International Workshop on Digital Libraries for Musicology. p. 9–16. DLfM 2016, Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2970044.2970054
Kaufmann, W.: The ragas of north India. Indiana University Press (1968)
Koduri, G.K., Gulati, S., Rao, P.: A survey of raaga recognition techniques and improvements to the state-of-the-art. Sound and Music Computing 38, 39–41 (2011)
Koduri, G.K., Gulati, S., Rao, P., Serra, X.: Rāga recognition based on pitch distribution methods. Journal of New Music Research 41(4), 337–350 (2012)
Koduri, G.K., Ishwar, V., Serrà, J., Serra, X.: Intonation analysis of rāgas in carnatic music. Journal of New Music Research 43(1), 72–93 (2014)
Krishnaswamy, A.: On the twelve basic intervals in south indian classical music. In: Audio Engineering Society Convention 115. Audio Engineering Society (2003)
Kumar, V., Pandya, H., Jawahar, C.: Identifying ragas in indian music. In: 2014 22nd International Conference on Pattern Recognition. pp. 767–772. IEEE (2014)
Lartillot, O., Toiviainen, P., Eerola, T.: A matlab toolbox for music information retrieval. In: Data analysis, machine learning and applications, pp. 261–268. Springer (2008)
Lee, K.: Automatic chord recognition from audio using enhanced pitch class profile. In: ICMC. Citeseer (2006)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. pp. 2–11 (2003)
Manjabhat, S.S., Koolagudi, S.G., Rao, K.S., Ramteke, P.B.: Raga and Tonic Identification in Carnatic Music. Journal of New Music Research 46(3), 229–245 (2017). https://doi.org/10.1080/09298215.2017.1330351
Murthy, Y.V.S., Koolagudi, S.G.: Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review. ACM Comput. Surv. 51(3) (Jun 2018). https://doi.org/10.1145/3177849
Pandey, G., Mishra, C., Ipe, P.: Tansen: A system for automatic raga identification. In: IICAI. pp. 1350–1363 (2003)
Pawar, M.Y., Mahajan, S.: Automatic tonic (shruti) identification system for indian classical music. In: Soft Computing and Signal Processing, pp. 733–742. Springer (2019)
Popescu, T., Widdess, R., Rohrmeier, M.: Western listeners detect boundary hierarchy in indian music: a segmentation study. Scientific reports 11(1), 1–14 (2021)
Quinlan, J.R.: C4. 5: programs for machine learning. Elsevier (2014)
Ranjani, H., Arthi, S., Sreenivas, T.: Carnatic music analysis: Shadja, swara identification and raga verification in alapana using stochastic models. In: 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). pp. 29–32. IEEE (2011)
Rao, P., Ross, J.C., Ganguli, K.K.: Distinguishing raga-specific intonation of phrases with audio analysis. ninad 26, 64 (2013)
Rao, P., Ross, J.C., Ganguli, K.K., Pandit, V., Ishwar, V., Bellur, A., Murthy, H.A.: Classification of melodic motifs in raga music with time-series matching. Journal of New Music Research 43(1), 115–131 (2014)
Rao, V., Rao, P.: Improving polyphonic melody extraction by dynamic programming based dual f0 tracking. In: Proc. of the 12th International Conference on Digital Audio Effects (DAFx), Como, Italy (2009)
Rao, V., Rao, P.: Vocal melody extraction in the presence of pitched accompaniment in polyphonic music. IEEE transactions on audio, speech, and language processing 18(8), 2145–2154 (2010)
Rodríguez López, M., de Haas, B., Volk, A.: Comparing repetition-based melody segmentation models. In: Proceedings of the 9th Conference on Interdisciplinary Musicology (CIM14). pp. 143–148. SIMPK and ICCMR (2014)
Ross, J.C., Rao, P.: Detection of raga-characteristic phrases from hindustani classical music audio. In: Serra X, Rao P, Murthy H, Bozkurt B, editors. Proceedings of the 2nd CompMusic Workshop; 2012 Jul 12–13; Istanbul, Turkey. Barcelona: Universitat Pompeu Fabra; 2012. p. 133–138. Universitat Pompeu Fabra (2012)
Ross, J.C., Vinutha, T., Rao, P.: Detecting melodic motifs from audio for hindustani classical music. In: ISMIR. pp. 193–198 (2012)
Roy, S., Banerjee, A., Sanyal, S., Ghosh, D., Sengupta, R.: A study on raga characterization in indian classical music in the light of mb and be distribution. In: Journal of Physics: Conference Series. vol. 1896, p. 012007. IOP Publishing (2021)
Salamon, J., Gómez, E.: Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing 20(6), 1759–1770 (2012)
Salamon, J., Gómez, E., Bonada, J.: Sinusoid extraction and salience function design for predominant melody estimation. In: Proc. 14th Int. Conf. on Digital Audio Effects (DAFX-11). pp. 73–80 (2011)
Salamon, J., Gulati, S., Serra, X.: A multipitch approach to tonic identification in indian classical music. In: Gouyon F, Herrera P, Martins LG, Müller M. ISMIR 2012: Proceedings of the 13th International Society for Music Information Retrieval Conference; 2012 Oct 8–12; Porto, Portugal. Porto: FEUP Ediçoes; 2012. International Society for Music Information Retrieval (ISMIR) (2012)
Sengupta, R., Dey, N., Nag, D., Datta, A., Mukerjee, A.: Automatic tonic (sa) detection algorithm in indian classical vocal music. In: National Symposium on Acoustics. pp. 1–5 (2005)
Sengupta, R.: Study on some aspects of the “singer’s formant” in north indian classical singing. journal of Voice 4(2), 129–134 (1990)
Serra, X.: Creating research corpora for the computational study of music: the case of the compmusic project. In: AES 53rd International Conference: Semantic Audio; 2014 Jan 27–29; London, UK. New York: Audio Engineering Society; 2014. Article number 1-1 [9 p.]. Audio Engineering Society (2014)
Shetty, S., Achary, K.: Raga mining of indian music by extracting arohana-avarohana pattern. International Journal of Recent Trends in Engineering 1(1), 362 (2009)
Sinith, M., Murthy, K., Tripathi, S.: Raga recognition through tonic identification using flute acoustics. International Journal of Advanced Intelligence Paradigms 15(3), 273–286 (2020)
Smith, T.F., Waterman, M.S., et al.: Identification of common molecular subsequences. Journal of molecular biology 147(1), 195–197 (1981)
Sridhar, R., Geetha, T.: Swara indentification for south indian classical music. In: 9th International Conference on Information Technology (ICIT’06). pp. 143–144. IEEE (2006)
Sridhar, R., Geetha, T.: Raga identification of carnatic music for music information retrieval. International Journal of recent trends in Engineering 1(1), 571 (2009)
Srinivasamurthy, A., Serra, X.: A supervised approach to hierarchical metrical cycle tracking from audio music recordings. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 5217–5221. IEEE (2014)
Sun, X.: A pitch determination algorithm based on subharmonic-to-harmonic ratio. In: Sixth International Conference on Spoken Language Processing (2000)
Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of time-series motif from multi-dimensional data based on mdl principle. Machine Learning 58(2), 269–300 (2005)
Velankar, M., Deshpande, A., Kulkarni, P.: Melodic pattern recognition in indian classical music for raga identification. International Journal of Information Technology 13(1), 251–258 (2021)
Widdess, R.: Involving the performers in transcription and analysis: a collaborative approach to dhrupad. Ethnomusicology 38(1), 59–79 (1994)
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This research was funded under grant number ECR/2018/000204 by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), of the Government of India.
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Singh, Y., Biswas, A. (2023). Computational Approaches for Indian Classical Music: A Comprehensive Review. In: Biswas, A., Wennekes, E., Wieczorkowska, A., Laskar, R.H. (eds) Advances in Speech and Music Technology. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-18444-4_5
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