[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Music Transcription with ISA and HMM

  • Conference paper
  • First Online:
Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

We propose a new generative model for polyphonic music based on nonlinear Independent Subspace Analysis (ISA) and factorial Hidden Markov Models (HMM). ISA represents chord spectra as sums of note power spectra and note spectra as sums of instrument-dependent log-power spectra. HMM models note duration. Instrument-dependent parameters are learnt on solo excerpts and used to transcribe musical recordings as collections of notes with time-varying power and other descriptive parameters such as vibrato. We prove the relevance of our modeling assumptions by comparing them with true data distributions and by giving satisfying transcriptions of two duo recordings.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 56.99
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Vincent, E., Févotte, C., Gribonval, R.: A tentative typology of audio source separation tasks. In: Proc. ICA, pp. 715–720 (2003)

    Google Scholar 

  2. Eggink, J., Brown, G.: Application of missing feature theory to the recognition of musical instruments in polyphonic audio. In: Proc. ISMIR, pp. 125–131 (2003)

    Google Scholar 

  3. Abdallah, S., Plumbley, M.: An ICA approach to automatic music transcription. In: Proc. 114th AES Convention (2003)

    Google Scholar 

  4. Virtanen, T.: Sound source separation using sparse coding with temporal continuity objective. In: Proc. ICMC (2003)

    Google Scholar 

  5. Eronen, A.: Musical instrument recognition using ICA-based transform of features and discriminatively trained HMMs. In: Proc. ISSPA (2003)

    Google Scholar 

  6. Mitianoudis, N., Davies, M.: Intelligent audio source separation using Independent Component Analysis. In: Proc. 112th AES Convention (2002)

    Google Scholar 

  7. Roweis, S.: One microphone source separation. In: Proc. NIPS, pp. 793–799 (2000)

    Google Scholar 

  8. Ghahramani, Z., Jordan, M.: Factorial hidden Markov models. Machine Learning 29, 245–273 (1997)

    Article  Google Scholar 

  9. Penny, W., Everson, R., Roberts, S.: Hidden Markov Independent Components Analysis. In: Advances in Independent Component Analysis, Springer, Heidelberg (2000)

    Google Scholar 

  10. Hand, D., Yu, K.: Idiot’s bayes - not so stupid after all? International Statistical Review 69, 385–398 (2001)

    MATH  Google Scholar 

  11. Ostendorf, M., Digalakis, V., Kimball, O.: From HMMs to segment models: a unified view of stochastic modeling for speech recognition. IEEE Trans. on Speech and Audio Processing 4, 360–378 (1996)

    Article  Google Scholar 

  12. Vincent, E., Rodet, X.: Underdetermined source separation with structured source priors. In: Proc. ICA (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vincent, E., Rodet, X. (2004). Music Transcription with ISA and HMM. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_151

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30110-3_151

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics