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Probabilistic Time-Frequency Source-Filter Decomposition of Non-Stationary Signals

Abstract : Probabilistic modelling of non-stationary signals in the timefrequency (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.
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https://hal.inria.fr/hal-00945277
Contributor : Roland Badeau <>
Submitted on : Tuesday, March 25, 2014 - 9:01:27 AM
Last modification on : Wednesday, October 14, 2020 - 1:11:49 PM
Long-term archiving on: : Wednesday, June 25, 2014 - 10:43:08 AM

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Roland Badeau, Mark. D. Plumbley. Probabilistic Time-Frequency Source-Filter Decomposition of Non-Stationary Signals. EUSIPCO, 2013, Marrakech, Morocco. ⟨hal-00945277⟩

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