Polyphonic transcription by non-negative sparse coding of power spectra

Abstract

We present a system for adaptive spectral basis decomposition that learns to identify independent spectral features given a sequence of short-term Fourier spectra. When applied to recordings of polyphonic piano music, the individual notes are identified as salient features, and hence each short-term spectrum is decomposed into a sum of note spectra; the resulting encoding can be used as a basis for polyphonic transcription. The system is based on a probabilistic model equivalent to a form of noisy independent component analysis (ICA) or sparse coding with non-negativity constraints. We introduce a novel modification to this model that recognises that a short-term Fourier spectrum can be thought of as a noisy realisation of the power spectral density of an underlying Gaussian process, where the noise is essentially multiplicative and non-Gaussian. Results are presented for an analysis of a live recording of polyphonic piano music.

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Showing 1-10 of 16 references

Aapo Hyvärinen. Survey on Independent Component Analysis. Neural Computing Surveys

  • 1999
Highly Influential
6 Excerpts

Unsupervised analysis of polyphonic music using sparse coding in a probabilistic framework

  • A Samer, Mark D Abdallah, Plumbley
  • 2003
1 Excerpt

Towards Music Perception by Redundancy Reduction and Unsupervised Learning in Probabilistic Models

  • A Samer, Abdallah
  • 2002
1 Excerpt
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