Skip to main content

Music Signal Processing: A Literature Survey

  • Conference paper
  • First Online:
Advances in Speech and Music Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1320))

  • 541 Accesses

Abstract

Music has become an integral part of our lives today. With the digital revolution that has struck the world and with the growth of computational power, browsing and storage have become accessible and effective. Thus, audio signal processing became an emerging area, paving the way for many new areas of research. In this paper, an attempt is made to give an overview of existing areas of research in music signal processing. Existing methodologies in these respective areas are explained in detail. A brief overview of future perspectives is also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sharma M (2007) Musical Heritage of India. APH Publishing,

    Google Scholar 

  2. Mahesh A (2017) Origin of Swara. https://anuradhamahesh.wordpress.com/carnatic-music-lessons-2/15-mythological-origin-of-the-swara/ Accessed 3 June 2020

  3. Shankar SV (2002) Tala Anubhava of the music trinity. J Music Acad.

    Google Scholar 

  4. Upbeat labs (2018) A primer for carnatic talas. Accessed 3 June 2020

    Google Scholar 

  5. Balaji MJ (2020) Introduction to Talas and Sapta Tala System, 2008. Accessed 3 June 2020

    Google Scholar 

  6. Gulati S, Salamon J, Serra X (2012) 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

    Google Scholar 

  7. Kartomi MJ (1990) On concepts and classifications of musical instruments. University of Chicago Press, Chicago

    Google Scholar 

  8. Von Hornbostel EM, Sachs C (1961) Classification of musical instruments: translated from the original german by anthony baines and klaus p. wachsmann. Galpin Soc J

    Google Scholar 

  9. Wang D, Huang Q (2009) Single channel music source separation based on harmonic structure estimation, pp 848–851. https://doi.org/10.1109/ISCAS.2009.5117889

  10. Wang H, Wang Y, Wang W, Zhu B, Ma S (2011) Single channel polyphonic music signal separation based on Bayesian harmonic model. In: Proceedings of 4th international congress on image and signal processing, Shanghai, pp 2784–2787. https://doi.org/10.1109/CISP.2011.6100778

  11. Atlas L, Janssen C (2005) Coherent modulation spectral filtering for single-channel music source separation. In: Proceedings (ICASSP'05). IEEE international conference on acoustics, speech, and signal processing, 2005. vol 4. IEEE (2005)

    Google Scholar 

  12. Park T, Kang KO (2014) Background music separation for multichannel audio based on Inter-channel level vector sum. In: Proceedings of 18th IEEE international symposium on consumer electronics (ISCE 2014), JeJu Island, pp 1–2. https://doi.org/10.1109/ISCE.2014.6884340

  13. Pedersen MS et al (2008) Convolutive blind source separation methods. Springer handbook of speech processing. Springer, Berlin, Heidelberg, pp 1065–1094

    Google Scholar 

  14. Blouet R, Rapaport G, Févotte C (2008) Evaluation of several strategies for single sensor speech/music separation. In: Proceedings of IEEE international conference on acoustics, speech and signal processing—proceedings, pp 37–40. https://doi.org/10.1109/ICASSP.2008.4517540

  15. Ozerov A, Févotte C (2009) Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. In: Proceedings of IEEE transactions on audio, speech, and language processing, vol 18, no 3

    Google Scholar 

  16. Grais EM, Erdogan H (2011) Single channel speech music separation using nonnegative matrix factorization and spectral masks. In: Proceedings of 17th international conference on digital signal processing (DSP). IEEE (2011)

    Google Scholar 

  17. Virtanen T (2007) Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. Proc IEEE Trans Audio, Speech Lang Proc 15(3):1066–1074

    Article  Google Scholar 

  18. Schmidt MN, Mørup M (2006) Nonnegative matrix factor 2-D deconvolution for blind single channel source separation. In: Proceedings of international conference on independent component analysis and signal separation. Springer, Berlin, Heidelberg (2006)

    Google Scholar 

  19. Lefevre A, Bach F, Févotte C (2012) Semi-supervised {NMF} with time-frequency annotations for single-channel source separation. In: Proceedings of 13th international society for music information retrieval

    Google Scholar 

  20. Huang P-S, Kim M, Hasegawa-Johnson M, Smaragdis P (2015) Joint optimization of masks and deep recurrent neural networks for monaural source separation. In: Audio speech language processing, in proceedings of IEEE/ACM Transactions on. 23https://doi.org/10.1109/TASLP.2015.2468583

  21. Fukayama Y (2014) A modified Wiener filter suitable for separation of individual instrumental sounds in monaural music signals. In: Proceedings of IEEE conference on norbert wiener in the 21st Century (21CW), Boston, MA, 2014, pp. 1–4. https://doi.org/10.1109/NORBERT.2014.6893934

  22. Fukayama Y (2019) Separation of individual instrumental tones in monaural music signals applying a modified wiener filter and the gabor wavelet transform. In: Proceedings of the ISCIE international symposium on stochastic systems theory and its applications, vol 2019. The ISCIE symposium on stochastic systems theory and its applications

    Google Scholar 

  23. Le Roux J, Vincent E (2012) Consistent wiener filtering for audio source separation. Proc IEEE Sig Proc Lett 20(3):217–220

    Google Scholar 

  24. Gunawan D, Sen D (2009) Music source separation synthesis using multiple input spectrogram inversion. In: Proceedings of IEEE international workshop on multimedia signal processing

    Google Scholar 

  25. Bofill P, Zibulevsky M (2000) Blind separation of more sources than mixtures using sparsity of their short-time Fourier transform. In: Proceedings of ICA, vol 2000

    Google Scholar 

  26. Bofill P, Zibulevsky M (2001) Underdetermined blind source separation using sparse representations. Proc Sig Process 81(11):2353–2362

    Article  Google Scholar 

  27. Huang, P-S et al (2012) Singing-voice separation from monaural recordings using robust principal component analysis. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP)

    Google Scholar 

  28. Ikemiya Y, Yoshii K, Itoyama K (2015) Singing voice analysis and editing based on mutually dependent F0 estimation and source separation. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE

    Google Scholar 

  29. Liutkus A et al (2012) Adaptive filtering for music/voice separation exploiting the repeating musical structure. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE

    Google Scholar 

  30. Rafii Z, Pardo B (2012) Music/voice separation using the similarity matrix. In: Proceedings of ISMIR

    Google Scholar 

  31. Rafii Z, Pardo B (2012) Repeating pattern extraction technique (REPET): A simple method for music/voice separation. Proc IEEE Trans Audio Speech Lang Process 21(1):73–84

    Article  Google Scholar 

  32. Lin Y-P et al (2010) EEG-based emotion recognition in music listening. Proc IEEE Trans Biomed Eng 57(7):1798–1806

    Google Scholar 

  33. Markov K, Matsui T (2014) Music genre and emotion recognition using Gaussian processes. In: Proceedings of IEEE access 2

    Google Scholar 

  34. Fukayama S, Goto M (2016) Music emotion recognition with adaptive aggregation of Gaussian process regressors. In: Proceedings of international conference on acoustics, speech and signal processing (ICASSP). IEEE

    Google Scholar 

  35. Chordia P, Rae A (2007) Raag recognition using pitch-class and pitch-class dyad distributions. In: Proceedings of ISMIR

    Google Scholar 

  36. Sridhar R, Geetha TV (2009) Raga identification of carnatic music for music information retrieval. Proc Int J Recent Trends Eng

    Google Scholar 

  37. Dighe P, Karnick H, Raj B (2013) Swara histogram based structural analysis and identification of Indian classical ragas. In: Proceedings of ISMIR

    Google Scholar 

  38. Padmasundari G, Murthy HA (2017) Raga identification using locality sensitive hashing. In: Proceedings of twenty-third national conference on communications (NCC). IEEE

    Google Scholar 

  39. Dutta S, Krishnaraj Sekhar PV, Murthy HA (2015) Raga verification in carnatic music using longest common segment set. In: Proceedings of ISMIR (2015)

    Google Scholar 

  40. Katte (2013) Multiple techniques for raga 378 identification in Indian classical music. Int J Electron Comput Eng (2013)

    Google Scholar 

  41. Gulati S et al (2016) Phrase-based rāga recognition using vector space modeling. In: 2016 In proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE

    Google Scholar 

  42. Kan A, sankar AS, Sundhar S (2017) A comparison of machine learning approaches to classify tala COMP 562

    Google Scholar 

  43. Koduri GK et al (2014) Intonation analysis of rāgas in Carnatic music. J New Mus Res

    Google Scholar 

  44. Koduri GK, Joan SJ, Serra X (2012) Characterization of intonation in carnatic music by parametrizing pitch histograms. 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)

    Google Scholar 

  45. Subramanian M (2007) Carnatic ragam thodi-pitch analysis of notes and gamakams. J Sangeet Natak Akademi 41(1)

    Google Scholar 

  46. Guedes C, Trochidis K, Anantapadmanabhan A (2018) Modeling carnatic rhythm generation: a data driven approach based on rhythmic analysis. In: Proceedings of the 15th sound & music computing conference

    Google Scholar 

  47. Ganguli K, Guedes, C (2019) An approach to adding knowledge constraints to a data-driven generative model for Carnatic rhythm sequence. Trends Electr Eng 9(3)

    Google Scholar 

  48. Heshi R et al (2016) Rhythm and timbre analysis for carnatic music processing. In: Proceedings of 3rd international conference on advanced computing, networking and informatics. Springer, New Delhi

    Google Scholar 

  49. Trochidis K et al (2016) CAMeL: carnatic percussion music generation using n-gram models. In: Proceedings of 13th sound and music computing conference (SMC), Hamburg, Germany, vol 31

    Google Scholar 

  50. Sebastian J, Murthy HA (2017) Onset detection in composition items of carnatic music. In: Proceedings of international society for music information retrieval ISMIR

    Google Scholar 

  51. Bellur A, Murthy HA (2013) A novel application of group delay function for identifying tonic in Carnatic music. In: Proceedings of 21st european signal processing conference (EUSIPCO 2013). IEEE (2013)

    Google Scholar 

  52. Samsekai Manjabhat S, et al (2017) Raga and tonic identification in carnatic music. J New Music Res 46(3)

    Google Scholar 

  53. Salamon J, Gulati S, Serra X (2012) A multipitch approach to tonic identification in indian classical music. In: Gouyon F, Herrera P, Martins LG, Müller M (eds) ISMIR 2012: Proceedings of the 13th international society for music information retrieval conference

    Google Scholar 

  54. Bellur A et al (2012) A knowledge based signal processing approach to tonic identification in Indian classical music. In: Serra X, Rao P, Murthy H, Bozkurt B (eds) Proceedings of the 2nd CompMusic Workshop; 2012 Jul 12–13; Istanbul, Turkey. Barcelona

    Google Scholar 

  55. Mammen S et al (2016) iSargam: music notation representation for Indian Carnatic music. EURASIP J Audio Speech Music Proc 2016(1)

    Google Scholar 

  56. Bellini X, Nesi P (2001) Wedel music format: an XML music notation format for emerging applications. In: Proceedings of first international conference on WEB delivering of music, IEEE computer society, Washington

    Google Scholar 

  57. Hoos HH, Hamel KA, Renz K, Kilian J (2001) Representing score-level music using the GUIDO music notation format. Computing in Musicology, MIT Press, Cambridge, p 12

    Google Scholar 

  58. Sridhar R, Geetha TV (2006) Swara identification for South Indian classical music. In: Proceedings of 9th international conference on information technology (ICIT'06), Bhubaneswar, pp 143–144

    Google Scholar 

  59. Prashanth TR, Venugopalan R (2011) Note identification in Carnatic Music from Frequency Spectrum In: Proceedings of international conference on communications and signal processing, Calicut, pp 87–91

    Google Scholar 

  60. Ranjani HG, Arthi S, Sreenivas TV (2011) Carnatic music analysis: Shadja, swara identification and rAga verification in AlApana using stochastic models. In: Proceedings of IEEE workshop on applications of signal processing to audio and acoustics (WASPAA), New Paltz, NY, pp 29–32

    Google Scholar 

  61. Bunt L, Stige B (2014) Music therapy: an art beyond words. Routledge

    Google Scholar 

  62. Maratos A et al (2008) Music therapy for depression. Cochrane database of systematic reviews, vol  1

    Google Scholar 

  63. Warwick A, Alvin J (1991) Music therapy for the autistic child. Oxford University Press (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pulijala, A.S., Gangashetty, S.V. (2021). Music Signal Processing: A Literature Survey. In: Biswas, A., Wennekes, E., Hong, TP., Wieczorkowska, A. (eds) Advances in Speech and Music Technology. Advances in Intelligent Systems and Computing, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-33-6881-1_1

Download citation

Publish with us

Policies and ethics