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Temporally-Constrained Convolutive Probabilistic Latent Component Analysis for Multi-pitch Detection

  • Emmanouil Benetos
  • Simon Dixon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)

Abstract

In this paper, a method for multi-pitch detection which exploits the temporal evolution of musical sounds is presented. The proposed method extends the shift-invariant probabilistic latent component analysis algorithm by introducing temporal constraints using multiple Hidden Markov Models, while supporting multiple-instrument spectral templates. Thus, this model can support the representation of sound states such as attack, sustain, and decay, while the shift-invariance across log-frequency can be utilized for multi-pitch detection in music signals that contain frequency modulations or tuning changes. For note tracking, pitch-specific Hidden Markov Models are also employed in a postprocessing step. The proposed system was tested on recordings from the RWC database, the MIREX multi-F0 dataset, and on recordings from a Disklavier piano. Experimental results using a variety of error metrics, show that the proposed system outperforms a non-temporally constrained model. The proposed system also outperforms state-of-the art transcription algorithms for the RWC and Disklavier datasets.

Keywords

Music signal analysis probabilistic latent component analysis hidden Markov models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emmanouil Benetos
    • 1
  • Simon Dixon
    • 1
  1. 1.Centre for Digital MusicQueen Mary University of LondonLondonUK

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