Single-Sensor Audio Source Separation Using Classification and Estimation Approach and GARCH Modeling

摘要

In this paper, we propose a new algorithm for single-sensor audio source separation of speech and music signals, which is based on generalized autoregressive conditional heteroscedasticity (GARCH) modeling of the speech signals and Gaussian mixture modeling (GMM) of the music signals. The separation of the speech from the music signal is obtained by a simultaneous classification and estimation approach, which enables one to control the tradeoff between residual interference and signal distortion. Experimental results on mixtures of speech and piano music signals have yielded an improved source separation performance compared to using Gaussian mixture models for both signals. The tradeoff between signal distortion and residual interference is controlled by adjusting some cost parameters, which are shown to determine the missed and false detection rates in the proposed classification and estimation approach.