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Automatic Music Transcription: From Monophonic to Polyphonic

  • Fabrizio Argenti
  • Paolo Nesi
  • Gianni Pantaleo
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 74)

Abstract

Music understanding from an audio track and performance is a key problem and a challenge for many applications ranging from: automated music transcoding, music education, interactive performance, etc. The transcoding of polyphonic music is a one of the most complex and still open task to be solved in order to become a common tool for the above mentioned applications. Techniques suitable for monophonic transcoding have shown to be largely unsuitable for polyphonic cases. Recently, a range of polyphonic transcoding algorithms and models have been proposed and compared against worldwide accepted test cases such as those adopted in the MIREX competition. Several different approaches are based on techniques such as: pitch trajectory analysis, harmonic clustering, bispectral analysis, event tracking, nonnegative matrix factorization, hidden Markov model. This chapter analyzes the evolution of music understanding algorithms and models from monophonic to polyphonic, showing and comparing the solutions, while analysing them against commonly accepted assessment methods and formal metrics.

Keywords

Hide Markov Model Negative Matrix Factorization Nonnegative Matrix Factorization Music Information Retrieval Musical Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Argenti, F., Nesi, P., Pantaleo, G.: Automatic Transcription of Polyphonic Music Based on Constant-Q Bispectral Analysis for MIREX 2009. In: Proc. of 10th ISMIR Conference (2009)Google Scholar
  2. 2.
    Bello, J.P.: Towards the Automated Analysis of Simple Polyphonic Music: A Knowledge-based Approach. PhD Thesis (2003)Google Scholar
  3. 3.
    Bello, J.P., Daudet, L., Sandler, M.B.: Automatic piano transcription using frequency and time-domain information. IEEE Transactions on Audio, Speech, and Language Processing 14(6), 2242–2251 (2006)CrossRefGoogle Scholar
  4. 4.
    Ortiz-Berenguer, L.I., Casajús-Quirós, F.J., Torres-Guijarro, S.: Multiple piano note identification using a spectral matching method with derived patterns. Journal of Audio Engineering Society 53(1/2), 32–43 (2005)Google Scholar
  5. 5.
    Bregman, A.: Auditory Scene Analysis.The MIT Press, Cambridge (1990)Google Scholar
  6. 6.
    Brossier, P.M.: Automatic Annotation of Musical Audio for Interactive Applications. PhD Thesis, Centre for Digital Music Queen Mary, University of London (2006)Google Scholar
  7. 7.
    Bruno, I., Nesi, P.: Automatic Music Transcription Supporting Different Instruments. Journal of New Music Research 34(2), 139–149 (2005)CrossRefGoogle Scholar
  8. 8.
    Cemgil, A.T., Kappen, H.J., Barber, D.: A Generative Model for Music Transcription. IEEE Transaction on Audio, Speech and Language Processing 14(2), 679–694 (2006)CrossRefGoogle Scholar
  9. 9.
    Chang, W.C., Su, A.W.Y., Yeh, C., Roebel, A., Rodet, X.: Multiple F0 Tracking Based on a High Order HMM Model. In: Proc. of the 11th Int. Conference on Digital Audio Effects, DAFx 2008 (2008)Google Scholar
  10. 10.
    Chafe, C., Jaffe, D.: Source separation and note identification in polyphonic music. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 1986), vol. 11, pp. 1289–1292 (1986)Google Scholar
  11. 11.
    Cont, A., Shlomo, D., Wessel, D.: Realtime multiple-pitch and multiple-instrument for music signals using sparse non-negative constraints. In: Proc. of 10th Int. Conference of Digital Audio Effects, DAFx 2007 (2007)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Duan, Z., Zhang, Y., Zhang, C., Shi, Z.: Unsupervised single-channel music source separation by average harmonic structure modeling. IEEE Transactions on Audio, Speech and Language Processing 16(4), 766–778 (2008)CrossRefGoogle Scholar
  14. 14.
    Dubois, C., Davy, M.: Joint detection and tracking of time-varying harmonic components: a general bayesian framework. IEEE Transactions on Audio, Speech and Language Processing 15(4), 1283–1295 (2007)CrossRefGoogle Scholar
  15. 15.
    Duan, Z., Han, J., Pardo, B.: Harmonically Informed Multi-pitch Tracking. In: Proc. of 10th International Society for Music Information Retrieval Conference, ISMIR 2009 (2009)Google Scholar
  16. 16.
    Ellis, D.P.W.: Prediction-driven Computational Auditory Scene Analysis. PhD Thesis, Massachusetts Institute of Technology (1996)Google Scholar
  17. 17.
    Fernández-Cid, P., Casajús-Quirós, F.J.: Multi-pitch estimation for Polyphonic Musical Signals. In: Proc. Of IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP 1998), vol. 6, pp. 3565–3568 (1998)Google Scholar
  18. 18.
    Fletcher, N.F., Rossing, T.D.: The physics of musical instruments, 2nd edn. Springer, New York (1998)zbMATHGoogle Scholar
  19. 19.
    Friedman, D.H.: Multichannel Zero-Crossing-Interval Pitch Estimation. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 1979), vol. 4, pp. 764–767 (1979)Google Scholar
  20. 20.
    Gerhard, D.: Pitch Extraction and Fundamental Frequency: History and Current Techniques. Tech. Report TR-CS 2003-06, Dep. of Computer Science, University or Regina, Canada (2003)Google Scholar
  21. 21.
    Godsill, S.J., Davy, M.: Bayesian Harmonic Models for Musical Signal Analysis. Bayesian Statistics 7, 105–124 (2003)MathSciNetGoogle Scholar
  22. 22.
    Godsill, S.J., Davy, M., Idier, J.: Bayesian analysis of polyphonic western tonal music. Journal of the Acoustical Society of America 119(4), 2498–2517 (2006)CrossRefGoogle Scholar
  23. 23.
    Gold, B., Rabiner, L.R.: Parallel Processing Techniques for Estimating Pitch Periods of Speech in the Time Domain. Journal of Acoustic Society of America 46(2), 442–448 (1969)CrossRefGoogle Scholar
  24. 24.
    Goto, M.: A robust predominant-f0 estimation method for real-time detection of melody and bass lines in cd recordings. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2000), Istanbul, Turkey, vol. 2, pp. 757–760 (2000)Google Scholar
  25. 25.
    Goto, M.: A real-time music-scene-description system: Predominant-F0 estimation for detecting melody and bass lines in real-world audio signals. Speech Communication - ISCA Journal 43(4), 311–329 (2004)CrossRefGoogle Scholar
  26. 26.
    Hawley, M.: Structure out of sound. Ph.D. thesis, MIT Media Laboratory, Cambridge, Massachusetts (1993)Google Scholar
  27. 27.
    Kameoka, H., Nishimoto, T., Sagayama, S.: A multipitch analyzer based on harmonic temporal structured clustering. IEEE Transactions on Audio, Speech, and Language Processing 15(3), 982–994 (2007)CrossRefGoogle Scholar
  28. 28.
    Kashino, K., Tanaka, H.: A Sound Source Separation System Using Spectral Features Integrated by Dempster’s Law of Combination. Annual Report of the Engineering Research Institute, vol. 52. University of Tokyo (1992)Google Scholar
  29. 29.
    Kashino, K., Tanaka, H.: A Sound Source Separation System with the Ability of Automatic Tone Modeling. In: Proc. of International Computer Music Conference (ICMC 1993), pp. 248–255 (1993)Google Scholar
  30. 30.
    Kashino, K., Nakadai, K., Kinoshita, T., Tanaka, H.: Application of Bayesian Probability Network to Music Scene Analysis. In: Computational Auditory Scene Analysis Workshop (IJCAI 1995), pp. 32–40 (1995)Google Scholar
  31. 31.
    Katayose, H., Inokuchi, S.: The KANSEI Music System. Computer Music Journal 13(4), 72–77 (1989)CrossRefGoogle Scholar
  32. 32.
    Klapuri, A.P.: Multiple fundamental frequency estimation based on harmonicity and spectral smoothness. IEEE Transactions on Speech and Audio Processing 11(6), 804–816 (2003)CrossRefGoogle Scholar
  33. 33.
    Klapuri, A.P.: Signal Processing Methods for the Automatic Transcription of Music. PhD thesis, Tampere University of Technology (2004)Google Scholar
  34. 34.
    Klapuri, A.P.: Automatic Music Transcription as We Know it Today. Journal of New Music Research 2004 33(3), 269–282 (2004)CrossRefGoogle Scholar
  35. 35.
    Klapuri, A.P.: A perceptually motivated multiple-f0 estimation method. In: Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 291–294 (2005)Google Scholar
  36. 36.
    Klapuri, A.P.: Multipitch analysis of polyphonic music and speech signals using an auditory model. IEEE Transactions on Audio, Speech, and Language Processing 16(2), 255–266 (2008)CrossRefGoogle Scholar
  37. 37.
    Maher, R.C.: Evaluation for a Method for Separating Digitized Duet Signals. Journal of Acoustic Engineering Society 38(12), 956–979 (1990)Google Scholar
  38. 38.
    Markel, J.D.: The SIFT Algorithm for Fundamental Frequency Estimation. IEEE Transactions on Audio and Electroacoustics 16, 367–377 (1972)CrossRefGoogle Scholar
  39. 39.
    Marolt, M.: SONIC: Transcription of polyphonic piano music with neural networks. In: Workshop on Current Research Directions in Computer Music, Barcelona, Spain, pp. 217–224 (2001)Google Scholar
  40. 40.
    Marolt, M.: Networks of adaptive oscillators for partial tracking and transcription of music recordings. Journal of New Music Research 33(1), 49–59 (2004)CrossRefGoogle Scholar
  41. 41.
    Martin, K.D.: A blackboard system for automatic transcription of simple polyphonic music. Perceptual Computing Technical Report 385, MIT Media Lab (1996)Google Scholar
  42. 42.
    Martin, K.D.: Automatic Transcription of Simple Polyphonic Music: Robust Front End Processing. Technical Report #399, MIT Media Lab, Perceptual Computing Section, The MIT Press (1996)Google Scholar
  43. 43.
    Meddis, R., O’Mard, L.: A Unitary Model of Pitch Perception. The Journal of the Acoustical Society of America 102(3), 1811–1820 (1997)CrossRefGoogle Scholar
  44. 44.
    Miller, N.J.: Pitch detection by data reduction. IEEE Transaction on Audio, Speech and Language Processing 23(1), 72–79 (1975)CrossRefGoogle Scholar
  45. 45.
    Moorer, J.: On the transcription of musical sound by computer. Computer Music Journal 1(4), 32–38 (1977)Google Scholar
  46. 46.
    Nawab, S.H., Ayyash, S.A., Wotiz, R.: Identification of musical chords using constant-q spectra. In: IEEE Proc. on Acoustic, Speech and Signal Processing (ICASSP 2001), vol. 5, pp. 3373–3376 (2001)Google Scholar
  47. 47.
    Peretz, I., Coltheart, M.: Modularity of music processing. Nature Neuroscience 6(7), 688–691 (2003)CrossRefGoogle Scholar
  48. 48.
    Pertusa, A., Iñesta, J.M.: Multiple Fundamental Frequency estimation using Gaussian smoothness. In: Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008, Las Vegas, USA, pp. 105–108 (2008)Google Scholar
  49. 49.
    Piszczalski, M., Galler, B.: Automatic music transcription. Computer Music Journal 1(4), 24–31 (1977)Google Scholar
  50. 50.
    Piszczalski, M., Galler, B.: Automatic music transcription. Computer Music Journal 66(3), 710–720 (1979)Google Scholar
  51. 51.
    Poliner, G.E., Ellis, D.P.W.: A Discriminative Model for Polyphonic Piano Transcription. IEEE Transaction on Audio, Speech and Language Processing 14(4), 1247–1256 (2007)CrossRefGoogle Scholar
  52. 52.
    Rabiner, L.R.: On the Use of Autocorrelation Analysis for Pitch Detection. IEEE Transaction on Acoustics, Speech and Signal Processing 25(1), 24–33 (1977)CrossRefGoogle Scholar
  53. 53.
    Rabiner, L.R.: A Comparative Performance Study of Several Pitch Detection Algorithms. IEEE Transaction on Acoustics, Speech and Signal Processing 24(5), 399–418 (1977)CrossRefGoogle Scholar
  54. 54.
    Raczynksi, S., Ono, N., Sagayama, S.: Multipitch analysis with harmonic nonnegative matrix approximation. In: Proc. of the 8th International Conference on Music Information Retrieval (ISMIR 2007), pp. 381–386 (2007)Google Scholar
  55. 55.
    Raphael, C.: Automatic transcription of piano music. In: Proc. on 3rd Int. Conf. on Music Information Retrieval, pp. 15–19 (2002)Google Scholar
  56. 56.
    Ryynänen, M.P., Klapuri, A.P.: Polyphonic Music Transcription Using Note Event Modeling. In: Proc. of 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, October 16-19, pp. 319–322 (2005)Google Scholar
  57. 57.
    Ryynänen, M.P., Klapuri, A.P.: Automatic bass line transcription from streaming polyphonic audio. In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), vol. 4, pp. 1437–1440 (2007)Google Scholar
  58. 58.
    Slaney, M., Lyon, R.F.: A Perceptual Pitch Detector. In: Proc. of IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP 1990), vol. 1, pp. 357–360 (1990)Google Scholar
  59. 59.
    Smaragdis, P., Brown, J.C.: Non-negative matrix factorization for polyphonic music transcription. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz (NY), pp. 177–180 (2003)Google Scholar
  60. 60.
    Tolonen, T., Karjalainen, M.: A computationally efficient multipitch analysis model. IEEE Transactions on Speech and Audio Processing 8(6), 708–716 (2000)CrossRefGoogle Scholar
  61. 61.
    Vincent, E., Bertin, N., Badeau, R.: Harmonic and Inharmonic Nonnegative Matrix Factorization for Polyphonic Pitch Transcription. In: Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2008), pp. 109–112 (2008)Google Scholar
  62. 62.
    Virtanen, T.: Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. In: Proc. of IEEE Int. Conf. on Computational Intelligence for Measurement Systems and Applications, vol. 15(3), pp. 1066–1074 (2007)Google Scholar
  63. 63.
    Yeh, C.: Multiple Fundamental Frequency Estimation of Polyphonic Recordings. PhD Thesis, Université Paris VI (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabrizio Argenti
    • 1
  • Paolo Nesi
    • 1
  • Gianni Pantaleo
    • 1
  1. 1.Department of Systems and InformaticsUniversity of FlorenceFlorenceItaly

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