PROBABILISTIC TIME-FREQUENCY SOURCE-FILTER DECOMPOSITION OF NON-STATIONARY SIGNALS

被引：0
|
作者：
Badeau, Roland [1 ]
Plumbley, Mark D. [2 ]
机构：
[1] Telecom ParisTech, Inst Mines Telecom, CNRS LTCI, Paris, France
[2] Univ London Queen Mary, Ctr Digital Music, Sch Elect Engn & Comp Sci, London E1 4NS, England
来源：
2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2013年
关键词：
Probabilistic modelling; Non-stationary processes; Time-frequency analysis; Source-filter models; Nonnegative matrix factorisation; NONNEGATIVE MATRIX FACTORIZATION; SEPARATION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
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页数：5
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