Musical instrument recognition in polyphonic audio using missing feature approach

IEEE Transactions on Audio, Speech and Language Processing

Volumn 21, Issue 9, 2013, Pages 1805-1817

  Giannoulis, Dimitrios ^a   Klapuri, Anssi ^b,c  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b Ovelin   (Finland)

^c TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Audio signal processing; bounded marginalization; harmonic sound; missing data techniques; musical instrument recognition

Indexed keywords

CLASSIFICATION PROCESS; COMPUTATIONALLY EFFICIENT; MARGINALIZATION; MISSING DATA TECHNIQUES; MUSICAL INSTRUMENT RECOGNITION; RECOGNITION ACCURACY; SPECTRAL COMPONENTS; SPECTRAL ENVELOPES;

ALGORITHMS; AUDIO SIGNAL PROCESSING; ESTIMATION; FACE RECOGNITION; FREQUENCY ESTIMATION;

MUSICAL INSTRUMENTS;

EID: 84880519009     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2013.2248720     Document Type: Article

References (45)

1. A. Bregman, Auditory Scene Analysis. Cambridge, MA, USA: MIT Press, 1990.
2. M. Cooke, P. Green, and M. Crawford, "Handling missing data in speech recognition" in Proc. 3rd Int. Conf. Spoken Lang. Process., 1994, pp. 1555-1558.
3. N. Fletcher and T. Rossing, The Physics of Musical Instruments. Berlin, Germany: Springer-Verlag, 1998.
4. A. Klapuri and M. Davy, Signal Processing Methods for Music Transcription. New York, NY, USA: Springer-Verlag, 2006.
5. E. Benetos, S. Dixon, D. Giannoulis, H. Kirchhoff, and A. Klapuri, "Automatic music transcription: Breaking the glass ceiling" in Proc. 13th Int. Soc. for Music Inf. Retrieval Conf., Oct. 2012.
6. M. Müller, D. Ellis, A. Klapuri, and G. Richard, "Signal processing for music analysis" IEEE J. Sel. Topics Signal Process., vol. 5, no. 6, pp. 1088-1110, Oct. 2011.
7. C. Joder, S. Essid, and G. Richard, "Temporal integration for audio classification with application to musical instrument classification" IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 1, pp. 174-186, Jan. 2009.
8. T. Kitahara, M. Goto, K. Komatani, T. Ogata, and H. G. Okuno, "Instrument identification in polyphonic music: Feature weighting to minimize influence of sound overlaps" EURASIP J. Appl. Signal Process., vol. 2007, pp. 1-15, 2007.
9. P. Herrera-Boyer, A. Klapuri, and M. Davy, "Automatic classification of pitched musical instrument sounds" in Signal Processing Methods for Music Transcription, A. Klapuri and M. Davy, Eds. New York, NY, USA: Springer, 2006, pp. 163-200.
10. D. Little and B. Pardo, "Learning musical instruments from mixtures of audio with weak labels" in Proc. 9th Int. Symp. Music Inf. Retrieval (ISMIR), Philadelphia, PA, USA, 2008.
11. S. Essid, G. Richard, and B. David, "Instrument recognition in polyphonic music based on automatic taxonomies" IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 68-80, Jan. 2006. (Pubitemid 43863454)
12. A. A. Livshin and X. Rodet, "Musical instrument identification in continuous recordings" in Proc. Int. Conf. Digital Audio Effects, Naples, Italy, 2004.
13. B. Kostek, "Musical instrument recognition and duet analysis employing music information retrieval techniques" Proc. IEEE, vol. 92, no. 4, pp. 712-729, Apr. 2004. (Pubitemid 40890745)
14. L. G. Martins, J. J. Burred, G. Tzanetakis, and M. Lagrange, "Polyphonic instrument recognition using spectral clustering" in Proc. 8th Int. Symp. Music Inf. Retrieval, Vienna, Austria, 2007.
15. J. J. Burred, A. Röbel, and T. Sikora, "Polyphonic musical instrument recognition based on a dynamic model of the spectral envelope" in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Taipei, Taiwan, 2009, pp. 173-176.
16. T. Heittola, A. Klapuri, and T. Virtanen, "Musical instrument recognition in polyphonic audio using source-filter model for sound separation" in Proc. 10th Int. Soc. for Music Inf. Retrieval Conf. (ISMIR), Kobe, Japan, 2009, pp. 327-332.
17. E. Vincent and X. Rodet, "Instrument identification in solo and ensemble music using independent subspace analysis" in Proc. 5th Int. Symp. Music Inf. Retrieval, Barcelona, Spain, 2004, pp. 576-581.
18. K. Itoyama, M. Goto, K. Komatani, T. Ogata, and H. Okuno, "Simultaneous processing of sound source separation and musical instrument identification using Bayesian spectral modeling" in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2011, pp. 3816-3819.
19. J. Wu et al., "Polyphonic pitch estimation and instrument identification by joint modeling of sustained and attack sounds" IEEE J. Sel. Topics Signal Process., vol. 5, no. 6, pp. 1124-1132, Oct. 2011.
20. P. Leveau, E. Vincent, G. Richard, and L. Daudet, "Instrument- specific harmonic atoms for mid-level music representation" IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 1, pp. 116-128, Jan. 2008.
21. J. Eggink and G. J. Brown, "A missing feature approach to instrument identification in polyphonic music" in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Hong Kong, China, 2003, pp. 553-556.
22. B. Raj and R. M. Stern, "Missing-feature approaches in speech recognition" IEEE Signal Process. Mag., vol. 22, no. 5, pp. 101-116, Sep. 2005. (Pubitemid 41488524)
23. J. Barker, "Missing data techniques: Recognition with incomplete spectrograms" in Techniques for Noise Robustness in Automatic Speech Recognition, T. Virtanen, R. Singh, and B. Raj, Eds. New York, NY, USA: Wiley, 2012.
24. J. Barker, M. Cooke, and D. P. W. Ellis, "Decoding speech in the presence of other sources" Speech Commun., vol. 45, no. 1, pp. 5-25, 2005. (Pubitemid 40034706)
25. J. G. A. Barbedo and G. Tzanetakis, "Musical instrument classification using individual partials" IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 1, pp. 111-122, Jan. 2011.
26. A. Klapuri, "Multiple fundamental frequency estimation by summing harmonic amplitudes" Proc. ISMIR, vol. 6, pp. 216-221, 2006.
27. A. Eronen, "Comparison of features for musical instrument recognition" in Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust., New Paltz, NY, USA, 2001, pp. 19-22. (Pubitemid 34080584)
28. G. Peeters, "A Large set of audio features for sound description (similarity and classification) in the CUIDADO project". Paris, France, IRCAM, Apr. 2004, Tech. Rep.
29. P. Smaragdis and J. C. Brown, "Non-negative matrix factorization for polyphonic music transcription" in Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust., 2003, pp. 177-180.
30. T. Virtanen, "Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria" IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 3, pp. 1066-1074, Mar. 2007.
31. C. Févotte, N. Bertin, and J.-L. Durrieu, "Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis" Neural Comput., vol. 21, no. 3, pp. 793-830, 2009.
32. T. Virtanen, R. Singh, and B. Raj, Eds., Techniques for Noise Robustness in Automatic Speech Recognition. New York, NY, USA: Wiley, 2012.
33. A. Vizinho, P. Green, M. Cooke, and L. Josifovski, "Missing data theory, spectral subtraction and signal-to-noise estimation for robust ASR: An integrated study" in Proc. Eurospeech, Budapest, Hungary, 1999, pp. 2407-2410.
34. M. El-Maliki and A. Drygajlo, "Missing features detection and handling for robust speaker verification" in Proc. Eurospeech, Budapest, Hungary, 1999, pp. 975-978.
35. V. Stahl, A. Fischer, and R. Bippus, "Quantile based noise estimation for spectral subtraction and wiener filtering" in Proc. Int. Conf. Acoust., Speech, Signal Process., Istanbul, Turkey, 2000, pp. 1875-1878.
36. M. Seltzer, B. Raj, and R. Stern, "A Bayesian classifier for spectrographic mask estimation for missing feature speech recognition" Speech Commun., vol. 43, no. 4, pp. 379-393, 2004.
37. D. Wang and G. J. Brown, Computational Auditory Scene Analysis: Principles, Algorithms and Applications. New York, NY, USA: Wiley-IEEE Press, 2006.
38. A. Klapuri, "Multipitch estimation and sound separation by the spectral smoothness principle" in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2001, pp. 3381-3384. (Pubitemid 32838672)
39. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition" Proc. IEEE, vol. 77, no. 2, pp. 257-289, Feb. 1989.
40. M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, "Rwc music database: Popular, classical, and jazz music databases" Proc. ISMIR, vol. 2, pp. 287-288, 2002.
41. F. Opolko and J. Wapnick, Mcgill University Master Samples. Montreal, QC, Canada: McGill Univ., Faculty of Music, 1989.
42. X. Huang, A. Acero, and H.-W. Hon, Spoken Language Processing. Upper Saddle River, NJ, USA: Prentice-Hall, 2001.
43. D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, "Speaker verification using adapted Gaussian mixture models" Digital Signal Process., vol. 10, pp. 19-41, 2000. (Pubitemid 30592166)
44. H. Abdi and L. Williams, "Principal component analysis" Wiley Interdisciplinary Rev.: Comput. Statist., vol. 2, no. 4, pp. 433-459, 2010.
45. J. T. Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering. Boca Raton, FL, USA: CRC, 1997.