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Query-by-example music information retrieval by score-based genre prediction and similarity measure

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Abstract

A topic of music information retrieval (MIR) field is query-by-example (QBE), which searches a popular music dataset using a user-provided query and aims to find the target song. Since this type of MIR has been generally used in online systems, retrieval time is also as important as accuracy. In this paper, we propose a QBE-based MIR system and investigate the impact of automatic music genre prediction on the performance of it, specifically on perspective of accuracy-time trade-off, using a score-based genre prediction method as well as similarity measures. The proposed system is evaluated on a dataset containing 6000 music pieces from six musical genres, and we show that how much improvement on the performance can be achieved in terms of accuracy and retrieval time, compared with a typical QBE-based MIR system that uses only similarity measures to find the user-desired song.

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References

  1. Dharani T, Aroquiaraj IL (2013) A survey on content based image retrieval. In: International conference on pattern recognition, informatics and mobile engineering (PRIME 2013) Salem, USA, pp 485–490

  2. Gong Y, Lazebnik S, Gordo A, Perronnin F (2013) Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans Pattern Anal Mach Intell 35(12):2916–2929

    Article  Google Scholar 

  3. Downie JS. The International Society of Music Information Retrieval. http://www.ismir.net/

  4. Ras ZW, Wieczorkowska A (2010) Advances in music information retrieval, 1st edn. Springer, Berlin

    Book  Google Scholar 

  5. Schedl M, Gómez E, Urbano J (2014) Music information retrieval: recent developments and applications. Found Trends Inf Retr 8(3):127–261

    Article  Google Scholar 

  6. Casey MA, Veltkamp R, Goto M, Leman M, Rhodes C, Slaney M (2008) Content-based music information retrieval: current directions and future challenges. Proc IEEE 96(4):668–696

    Article  Google Scholar 

  7. Helén M, Virtanen T (2010) Audio query by example using similarity measures between probability density functions of features. EURASIP J Audio Speech Music Process, pp 1–12

  8. Tsai W-H, Yu H-M, Wang H-M (2005) Query-by-example technique for retrieving cover versions of popular songs with similar melodies. In: 6th international conference on music information retrieval, London, UK. September 11–15, pp 183-190

  9. Suyoto ISH, Uitdenbogerd AL, Scholer F (2007) Effective retrieval of polyphonic audio with polyphonic symbolic queries. In: MIR ’07 Proceedings of the international workshop on multimedia information retrieval, pp 105–114

  10. Makhoul J, Kubala F, Leek T, Liu D, Nguyen L, Schwartz R, Srivastava A (2000) Speech and language technologies for audio indexing and retrieval. Proc IEEE 88(8):1338–1353

    Article  Google Scholar 

  11. Tsai W-H, Tu Y-M, Ma C-H (2012) An fft-based fast melody comparison method for query-by-singing/humming systems. Pattern Recogn Lett 33:2285–2291

    Article  Google Scholar 

  12. Yu H-M, Tsai W-H, Wang H-M (2008) A query-by-singing system for retrieving karaoke music. IEEE Trans Multimed 10(8):1626–1637

    Article  Google Scholar 

  13. Unal E, Chew E, Georgiou PG, Narayanan SS (2008) Challenging uncertainty in query by humming systems: a fingerprinting approach. IEEE Trans Audio Speech Lang Process 16(2):359–371

    Article  Google Scholar 

  14. Kaminskas M, Ricci F (2012) Contextual music information retrieval and recommendation: state of the art and challenges. Comput Sci Rev 6:89–119

    Article  Google Scholar 

  15. Schröder A, Keith M. Free database. http://www.freedb.org

  16. Kaye R. The Open Music Encyclopedia. https://musicbrainz.org

  17. Barton C, Inghelbrecht P, Wang A, Mukherjee D. Shazam Company. http://www.shazam.com/company

  18. Chuffart F. Musiwave. http://www.musiwave.net

  19. Born J. Neuros. www.neurostechnology.com

  20. Salamon J, Gómez E (2010) Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Trans Audio Speech Lang Process 20(6):1759–1770

    Article  Google Scholar 

  21. Itoyama K, Goto M, Komatani K, Ogata T (2010) Query-by-example music information retrieval by score informed source separation and remixing technologies. EURASIP J Adv Signal Process 2010:1–14

    Article  Google Scholar 

  22. Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302

    Article  Google Scholar 

  23. Silla CN, Koerich AL, Kaestner CAA (2008) Feature selection in automatic music genre classification. In: Tenth IEEE international symposium on multimedia (ISM 2008), Berkeley, CA, pp 39–44

  24. Dehkordi MB (2014) Music genre classification using spectral analysis and sparse representation of the signals. J Signal Process Syst 74:1–8

    Article  Google Scholar 

  25. Harb H, Chen L (2003) A query by example music retrieval algorithm. In: Proceedings of the 4th European workshop on image analysis for multimedia interactive services (WIAMIS ’03), pp 1–7

  26. Chathuranga D, Jayaratne L (2012) Musical genre classification using ensemble of classifiers. In: Fourth international conference on computational intelligence, modelling and simulation (CIMSIM 2012), Kuantan, pp 237–242

  27. Wang L, Huang S, Wang S, Liang J, Xu B (2008) Music genre classification based on multiple classifier fusion. In: Fourth international conference on natural computation, pp 580–583

  28. ISMIR audio description contest. http://ismir2004.ismir.net/genre_contest/index.htm

  29. Zwicker E, Fastl H (2013) Psychoacoustics: facts and models. Springer, Berlin

    Google Scholar 

  30. Termens EGi (2009) Audio content processing for automatic music genre classification: descriptors, databases, and classifiers. Ph.D. thesis, Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona

  31. Rabiner LR, Juang BH (1993) Fundamental of speech recognition prentice, 1st edn. Prentice Hall, Prentice

    Google Scholar 

  32. Meng A, Ahrendt P, Larsen J (2007) Temporal feature integration for music genre classification. IEEE Trans Audio Speech Lang Process 15(5):1654–1664

    Article  Google Scholar 

  33. Porter FC, Narsky I (2013) Statistical analysis techniques in particle physics, fits, density estimation and supervised learning. Wiley, London

    Google Scholar 

  34. Rokach L, Maimon O (2008) Data mining with decision trees: theory and applications. World Scientific, Singapore

    MATH  Google Scholar 

  35. Sutton CD (2005) Classification and regression trees, bagging, and boosting. Handb Stat 24:303–329

    Article  Google Scholar 

  36. Barros RC, Basgalupp MP, Carvalho ACPLF, Freitas AA (2012) A survey of evolutionary algorithms for decision-tree Induction. IEEE Trans Syst Man Cybern Part C Appl Rev 42(3):291–312

  37. Goldberger J, Gordon S, Greenspan H (2003) An efficient image similarity measure based on approximations of KL divergence between two Gaussian mixtures. In: Proceedings of the 9th IEEE international conference on computer vision (ICCV ’03). Nice, France, pp 487–493

  38. Kashino K, Kurozumi T, Murase H (2003) A quick search method for audio and video signals based on histogram pruning. IEEE Trans Multimed 5(3):348–357

    Article  Google Scholar 

  39. Helén M, Virtanen T (2007) Query by example of audio signals using Euclidean distance between Gaussian mixture models. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP ’07). Honolulu, Hawaii, USA, pp 225–228

  40. Mohajer K, Emami M, Hom J, McMahon K, Stonehocker T, Lucanegro C, Mohajer K, Arbabi A, Shakeri F. www.soundhound.com

  41. Gowan M. http://www.techhive.com/

  42. Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2007) Digital watermarking and steganography, 2nd edn. Morgan Kaufmann, Los Altos

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Nastaran Borjian

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  1. Nastaran Borjian
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Correspondence to Nastaran Borjian.

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Borjian, N. Query-by-example music information retrieval by score-based genre prediction and similarity measure. Int J Multimed Info Retr 6, 155–166 (2017). https://doi.org/10.1007/s13735-017-0125-z

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