PROBABILISTIC LATENT TENSOR FACTORIZATION FRAMEWORK FOR AUDIO MODELING

被引：0

作者：

Cemgil, Ali Taylan ^{[1
,2
]}

Simsekli, Umut ^{[1
]}

Subakan, Yusuf Cem ^{[2
]}

机构：

[1] Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey

[2] Bogazici Univ, Dept Elect & Elect Engn, TR-34342 Istanbul, Turkey

来源：

2011 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA) | 2011年

关键词：

Audio Modeling; Probabilistic Latent Tensor Factorization; Factor graphs; Statistical Inference; Message Passing; NONNEGATIVE MATRIX FACTORIZATION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper introduces probabilistic latent tensor factorization (PLTF) as a general framework for hierarchical modeling of audio. This framework combines practical aspects of graphical modeling of machine learning with tensor factorization models. Once a model is constructed in the PLTF framework, the estimation algorithm is immediately available. We illustrate our approach using several popular models such as NMF or NMF2D and provide extensions with simulation results on real data for key audio processing tasks such as restoration and source separation.

引用

页码：137 / 140

页数：4

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