Online Noisy Single-Channel Source Separation Using Adaptive Spectrum Amplitude Estimator and Masking

N. Tengtrairat, W. L. Woo, S. S. Dlay, Bin Gao

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

A novel single-channel source separation method is presented to recover the original signals given only a single observed mixture in noisy environment. The proposed separation method is an online adaptive process and independent of parameters initialization. In this paper, a noisy pseudo-stereo mixing model is developed by formulating an artificial mixture from the observed mixture where the signals are modeled by the autoregressive process. The proposed demixing process composes of two steps: First, the noisy mixing model is enhanced by selecting the time-frequency (TF) units of signal presence and computing the mixture spectral amplitude, and second, an adaptive estimation of the parameters associated with each source is computed frame-by-frame, which is then used to construct a TF mask for the separation process. To assess the performance of the proposed method, noisy mixtures of real-audio sources with nonstationary noise have been conducted under various SNRs. Experiments show that the proposed algorithm has yielded superior separation performance especially in low input SNR compared with existing methods.

Original languageEnglish
Article number7244216
Pages (from-to)1881-1895
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume64
Issue number7
Early online date7 Sep 2015
DOIs
Publication statusPublished - 1 Apr 2016
Externally publishedYes

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