By Topic

Polyphonic audio-to-score alignment based on Bayesian Latent Harmonic Allocation Hidden Markov Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Akira Maezawa ; Dept. of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Sakyo, 606-8501 Japan ; Hiroshi G. Okuno ; Tetsuya Ogata ; Masataka Goto

This paper presents a Bayesian method for temporally aligning a music score and an audio rendition. A critical problem in audio-to-score alignment is in dealing with the wide variety of timbre and volume of the audio rendition. In contrast with existing works that achieve this through ad-hoc feature design or careful training of tone models, we propose a Bayesian audio-to-score alignment method by modeling music performance as a Bayesian Hidden Markov Model, each state of which emits a Bayesian signal model based on Latent Harmonic Allocation. After attenuating reverberation, variational Bayes method is used to iteratively adapt the alignment, instrument tone model and the volume balance at each position of the score. The method is evaluated using sixty works of classical music of a variety of instrumentation ranging from solo piano to full orchestra. We verify that our method improves the alignment accuracy compared to dynamic time warping based on chroma vector for orchestral music, or our method employed in a maximum likelihood setting.

Published in:

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

22-27 May 2011