By Topic

Generative Spectrogram Factorization Models for Polyphonic Piano Transcription

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

3 Author(s)
Paul H. Peeling ; Eng. Dept., Cambridge Univ., Cambridge, UK ; A. Taylan Cemgil ; Simon J. Godsill

We introduce a framework for probabilistic generative models of time-frequency coefficients of audio signals, using a matrix factorization parametrization to jointly model spectral characteristics such as harmonicity and temporal activations and excitations. The models represent the observed data as the superposition of statistically independent sources, and we consider variance-based models used in source separation and intensity-based models for non-negative matrix factorization. We derive a generalized expectation-maximization algorithm for inferring the parameters of the model and then adapt this algorithm for the task of polyphonic transcription of music using labeled training data. The performance of the system is compared to that of existing discriminative and model-based approaches on a dataset of solo piano music.

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:18 ,  Issue: 3 )