NDLI: Two-stage blind audio source counting and separation of stereo instantaneous mixtures using Bayesian tensor factorisation

Content Provider	IET Digital Library
Author	Mirzaei, Sayeh Norouzi, Yaser Hamme, Hugo Van
Abstract	In this paper, the authors address the tasks of audio source counting and separation for two-channel instantaneous mixtures. This goal is achieved in two steps. First, a novel scheme is proposed for estimating the number of sources and the corresponding channel intensity difference (CID) values. For this purpose, an angular spectrum is evaluated as a function of the ratio of the magnitude spectrogram of the two channels and the peak locations of that spectrum are obtained. In the second stage, a new approach is developed for extracting the individual source signals exploiting a Bayesian non-parametric modelling. The mean field variational Bayesian approach is applied for inferring the unknown parameters. Classification is then performed on the inferred active CID values to obtain the individual source magnitude spectrograms. This way, the number of spectral components used for modelling each source is found automatically from the data. The Bayesian approach is compared with the standard Kullback–Leibler non-negative tensor factorisation method to illustrate the effectiveness of Bayesian modelling. The performance of the source separation is measured by obtaining the existing metrics for multichannel blind source separation evaluation. The experiments are performed on instantaneous mixtures from the dev2 database.
Starting Page	587
Ending Page	595
Page Count	9
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 8, Oct (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0404
Journal	IET Signal Processing
Publisher	The Institution of Engineering and Technology
Publisher Date	2015-09-24
Access Restriction	Open
Rights License	Creative Commons Attribution-Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Algebra Angular Spectrum Bayes Method Bayesian Nonparametric Modelling Bayesian Tensor Factorisation Channel Intensity Difference Individual Source Magnitude Spectrograms Individual Source Signal Kullback-Leibler Nonnegative Tensor Factorisation Method Matrix Decomposition Signal Processing And Detection Signal Processing Theory Source Separation Statistics Stereo Instantaneous Mixtures Separation Two-stage Blind Audio Source Counting
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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