TY - GEN
T1 - Probabilistic time-frequency source-filter decomposition of non-stationary signals
AU - Badeau, Roland
AU - Plumbley, Mark D.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Probabilistic modelling of non-stationary signals in the time-frequency (TF) domain has been an active research topic recently. Various models have been proposed, notably in the nonnegative matrix factorization (NMF) literature. In this paper, we propose a new TF probabilistic model that can represent a variety of stationary and non-stationary signals, such as autoregressive moving average (ARMA) processes, uncorrelated noise, damped sinusoids, and transient signals. This model also generalizes and improves both the Itakura-Saito (IS)-NMF and high resolution (HR)-NMF models.
AB - Probabilistic modelling of non-stationary signals in the time-frequency (TF) domain has been an active research topic recently. Various models have been proposed, notably in the nonnegative matrix factorization (NMF) literature. In this paper, we propose a new TF probabilistic model that can represent a variety of stationary and non-stationary signals, such as autoregressive moving average (ARMA) processes, uncorrelated noise, damped sinusoids, and transient signals. This model also generalizes and improves both the Itakura-Saito (IS)-NMF and high resolution (HR)-NMF models.
KW - Non-stationary processes
KW - Nonnegative matrix factorisation
KW - Probabilistic modelling
KW - Source-filter models
KW - Time-frequency analysis
M3 - Conference contribution
AN - SCOPUS:84901345924
SN - 9780992862602
T3 - European Signal Processing Conference
BT - 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PB - European Signal Processing Conference, EUSIPCO
T2 - 2013 21st European Signal Processing Conference, EUSIPCO 2013
Y2 - 9 September 2013 through 13 September 2013
ER -