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MIMVOGUE: modeling Indian music using a variable order gapped HMM

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Abstract

The computer-assisted music composition is an active research area since mid-1900. In this paper, we have applied the VOGUE model for designing musical sequence of bandish notations of raga Bhairav, a classical Indian music. Variable Order and Gapped hidden Markov model for unstructured elements can capture variable length dependencies with variable gaps in sequential data. In most of raga pattern, a particular pattern repeats itself which may be separated by variable length gaps. VOGUE mines the frequent patterns in raga having different length gaps. These mined patterns are used to model VOGUE for Indian music ragas. Furthermore, we analyzed the benefits of VOGUE model over the standard HMM. To the best of author’s knowledge, this is the very first attempt to model Indian classical music with variable order gapped HMM.

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References

  1. Benefits of music online available: https://www.bartleby.com/essay/Bene_ts-of-Music-P3CNQAPZVC. Accessed 23 March 2019

  2. Benetos E, Weyde T (2013) Explicit duration hidden Markov models for multiple-instrument polyphonic music transcription. In: In proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 4-8, pp. 269–274

  3. Berget GE (2017) Using hidden Markov models for musical chord prediction, Department of Mathematical Sciences, Norwegian University of Science and Technology, Norway

  4. Bouqata B, Carothers CD, Szymanski BK, Zaki MJ (2006) VOGUE: a novel variable order-gap state machine for modeling sequences. In: Proceedings of the European conference on principles and practice of knowledge discovery in databases (PKDD06), Berlin, Germany, 18-22, 42–54

  5. Chen MY, Kundu A, Srihari SN (1995) Variable duration hidden Markov model and morphological segmentation for handwritten word recognition. IEEE Trans Image Process 4(12):1675–1688

    Article  Google Scholar 

  6. Chen C, Liang J, Zhao H, Hu H, Tian J (2009) Factorial HMM and parallel HMM for gait recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 39(1):114–123

    Article  Google Scholar 

  7. Chen R, Shen W, Srinivasamurthy A, Chordia P (2012) Chord recognition using duration-explicit hidden Markov models. In: In proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal, 8-12, pp. 445–450

  8. Chithra S, Sinith MS, Gayathri A (2015) Music information retrieval for polyphonic signals using hidden Markov model. Procedia Comput Sci 46:381–387

    Article  Google Scholar 

  9. Chordia P, Sastry A, Senturk S (2016) Predictive tabla modelling using Variable-length Markov and hidden Markov models. J New Music Res 40(2):105–118

    Article  Google Scholar 

  10. Corrêa DC, Jüngling T, Small M (2020) Quantifying the generalization capacity of Markov models for melody prediction. Phys A: Stat Mech Appl 124351:1–13

    Google Scholar 

  11. Coviello E, Chan AB, Lanckriet G (2010) Time series models for semantic music annotation. IEEE Trans Audio Speech Lang Process 19(5):1343–1359

    Article  Google Scholar 

  12. Dalin-Volsing S (2017) Classification of musical genres using hidden Markov model, Centre for Mathematical Sciences. Lund University, Lund

    Google Scholar 

  13. Dang S, Chaudhury S, Lall B, Roy PK (2017) Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data. J Neurosci Methods 278:87–100

    Article  Google Scholar 

  14. Das D, Choudhury M (2005) Finite state models for generation of Hindustani classical music. In: Proceedings of 11th international symposium on Frontiers of research in speech and music (FRSM), Bhubaneswar, Orissa, India, 6-7, pp. 59–64

  15. Dharini D, Revathi A, Kalaivani M (2018) CD-HMM Modeling for raga identification. In: Proceedings of 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), pp. 486–489

  16. Hoffman M, Cook P, Blei D (2008) Data-driven Recomposition using the hierarchical Dirichlet process hidden Markov model. In: proceedings of 32nd international computer music conference (ICMC), Belfast, Ireland, UK, 24-29, pp. 1–7

  17. Kerr R (2011) Melodic analysis using hierarchical hidden Markov models, Master of science Thesis, School of Informatics, University of Edinburgh, Scotland

  18. Khorasani A, Daliri MR, Pooyan M (2016) Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model. Biomed Eng/Biomed Tech 61(1):119–126

    Article  Google Scholar 

  19. Li T, Choi M, Fu K, Lin L (2019) Music sequence prediction with mixture hidden markov models. In: Proceedings of IEEE international conference on big data, Los Angeles, USA, 9-12, pp. 6128–6132

  20. Lin J, Zhang B 2018 A music retrieval method based on hidden markov model. In: Proceedings of international conference on intelligent transportation, big Data & Smart City (ICITBS), Xiamen, China, 25-26, pp. 732–735

  21. Liu Z, Wang S (2017) Decoding Chinese stock market returns: three-state hidden semi-markov model. Pac Basin Financ J 44:127–149

    Article  Google Scholar 

  22. McCormack J (1996) Grammar-based music composition. Complex Syst 3:321–336

    Google Scholar 

  23. Nakamura E, Nakamura T, Saito Y, Ono N, Sagayama S (2014) Outer-product hidden Markov model and polyphonic MIDI score following. J New Music Res 43(2):183–201

    Article  Google Scholar 

  24. Nakamura E, Cuvillier P, Cont A, Ono N, Sagayama S (2015) Autoregressive hidden semi-markov model of symbolic music performance for score following. In: Proceedings of 16th International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain, 26-30, pp. 392–398

  25. Nakano M, Ohishi Y, Kameoka H, Mukai R, Kashino K (2014) Mondrian hidden Markov model for music signal processing. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP), Florence, Italy, 4-9, pp. 2424–2428

  26. Ozerov A, Fevotte C, Charbit M (2009) Factorial scaled hidden Markov model for polyphonic audio representation and source separation. In: Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA'09), New Paltz, New York, pp. 121–124.

  27. Pandey G, Mishra C, Ipe P (2003) Tansen: a system for automatic raga identification. In: Proceedings of 1st Indian international conference on artificial intelligence (IICAI), Hyderabad, India, 18-20, pp. 1350–1363

  28. Pikrakis A, Theodoridis S, Kamarotos D (2006) Classification of musical patterns using variable duration hidden Markov models. IEEE Trans Audio Speech Lang Process 14(5):1795–1807

    Article  Google Scholar 

  29. Qi Y, Paisley JW, Carin L (2007) Music analysis using hidden Markov mixture models. IEEE Trans Signal Process 55(11):5209–5224

    Article  MathSciNet  Google Scholar 

  30. Raga Bhairav Online Available: https://www.surgyan.com/raagbhairav.htm. Accessed 23 March 2019

  31. Ren L, Dunson D, Lindroth S, Carin L (2010) Dynamic nonparametric Bayesian models for analysis of music. J Am Stat Assoc 105:458–472

    Article  MathSciNet  Google Scholar 

  32. Román-Gálvez R, Román-Roldán R, Martínez-Aroza J, Gómez-Lopera JF (2015) Semi-hidden Markov models for generation and analysis of sequences. Math Comput Simul 118:320–328

    Article  MathSciNet  Google Scholar 

  33. Sekhar PK, Viraraghavan VS, Sankaran S, Murthy HA (2017) An approach to transcription of varnams in carnatic music using hidden markov models. In: Proceedings of 23rd National Conference on communications (NCC), Chennai India, 2-4, pp. 1–6

  34. Senturk S (2011) Computational modeling of improvisation in Turkish folk music using variable-length Markov models. In: Georgia Institute of Technology, Atlanta, Georgia

  35. Senturk S, Chordia P (2011) Modeling melodic improvisation in Turkish folk music using variable-length Markov models, in: proceedings of 12th International Society for Music Information Retrieval Conference (ISMIR), Miami, Florida, USA, 24-28, pp. 269–274

  36. Shen J, Meng W, Yan S, Pang H, Hua X (2010) Effective music tagging through advanced statistical modeling. In: Proceedings of the 33rd international ACM SIGIR conference on Research and Development in information retrieval, Geneva, Switzerland, 19-23, pp. 635–642

  37. Shen J, Pang H, Wang M, Yan S (2012) Modeling concept dynamics for large scale music search. In: Proceedings of the 35th international ACM SIGIR conference on Research and Development in information retrieval, Portland, Oregon, USA, 12-16 august 2012, pp. 455-464

  38. Smith K (2002) Hidden Markov Models in Bioinformatics with Application to Gene Finding in Human DNA. Mach Learn Proj 308–761

  39. Stanculescu I, Williams CK, Freer Y (2014) Autoregressive hidden Markov models for the early detection of neonatal sepsis. IEEE J Biomed Health Inform 18(5):1560–1570

    Article  Google Scholar 

  40. Wang CI, Dubnov S (2017) Context-aware hidden Markov models of jazz music with variable Markov oracle. In: Proceedings of 5th international workshop on musical Metacreation (MUME2017), Atlanta, USA, 19-23, pp. 1–8

  41. Weiland M, Smaill A, Nelson P (2005) Learning musical pitch structures with hierarchical hidden Markov model, University of Edinburgh, Scotland, UK

  42. What is bandish in music Online Available: https://www.thehindubusinessline.com/blink/watch/Whatisbandish/article20904915.ece. Accessed 23 March 2019

  43. Wysocki D, Graci C (2015) Modeling music using hidden Markov models

  44. Xiao Q, Fang Y, Liu Q, Zhou S (2018) Online machine health prognostics based on modified duration-dependent hidden semi-Markov model and high-order particle filtering. Int J Adv Manuf Technol 94(1–4):1283–1297

    Article  Google Scholar 

  45. Yanchenko A (2017) Classical music composition using hidden Markov models, Department of Statistical Science, Duke University, Durham, North Carolina

  46. Zaki MJ, Carothers CD, Szymanski BK (2010) VOGUE: a variable order hidden Markov model with duration based on frequent sequence mining. ACM Trans Knowl Discov Data 4(1):1–31

    Article  Google Scholar 

  47. Zhai CX (2003) A brief note on the hidden Markov models (HMMs), Department of Computer Science, University of Illinois at Urbana-champaign, IL, USA

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Bhavya Mor, Sunita Garhwal & Ajay Kumar

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  1. Bhavya Mor
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  2. Sunita Garhwal
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  3. Ajay Kumar
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Corresponding author

Correspondence to Ajay Kumar.

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Appendix

Appendix

We designed VOGUE model (Fig. 1) for bandishes of four composition of raga Bhairav from different compositions. From the following sequences(Z), subsequences (F)are extracted (Table 2).

  • Composition 1:

Table 7 Initial state probabilities of VOGUE model for raga Bhairav
Full size table

GMDb − P − DbMP − P − MPDbPMG − G − GRbGMPPGGMGRb − S − SRbGMP − DPMPDbNSS'NDbPMGRbSMMPDbNNSS'N − S'RbSNSS'Rb − RbRbRb − RbRbSRbGMRbRbSS'S'S'NDbDbP − S'NDPMGRbSSRbGMP − DbPMPDbNSS'NDbPMRbS

  • Composition 2:

GMM − PMGM − PMGGRbGMPMGGGMRbRbSNSGMPDbNS'S'NDbNNNDbPMPMGMMMMDbDbDbMMMMDbDbDbS'S'NRbS'DbDbDbNS'S'S'NNS'DbPGGGRbGMPMGMPMRbRbSNSGMPDbNS'S'NDbNNNDbPMPMG

  • Composition 3:

GMDbDbP − DbMMPDbPGMRb − S − NSGMPDbNS'DbS'NDbPMGMP − PPDb − N − S' − S'S'NS'S' − NS'GPRbRbS'S'DbNS'NDbPMG

  • Composition 4:

GMDbDbP − DbGDb − PMPM − G − GGMGRb − GPMGMGRb − S − NSGMPDbNS'Rb − S'NDbPM − PPP − DbDbNNS'S'S'S'Rb − S' − Db − Db − NNS'S'Rb − S'S'NS'DbPGMPDbS'NDbPMGMGRb − SSNSGMPDbNS'Rb − S'NDbPM−

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Mor, B., Garhwal, S. & Kumar, A. MIMVOGUE: modeling Indian music using a variable order gapped HMM. Multimed Tools Appl 80, 14853–14866 (2021). https://doi.org/10.1007/s11042-020-10303-y

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  • DOI: https://doi.org/10.1007/s11042-020-10303-y

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