Catalog-based single-channel speech-music separation for automatic speech recognition

Abstract

In this study, we analyze the effect of the catalog-based single-channel speech-music separation method, which we proposed previously, on speech recognition performance. In the proposed method, assuming that we know a catalog of the background music, we developed a generative model for the superposed speech and music spectrograms. We represent the speech spectrogram by a Non-negative Matrix Factorization (NMF) model and the music spectrogram by a conditional Poisson Mixture Model (PMM). In this paper, we propose to recover the speech signals from the mixed signal in time-domain by detecting the active catalog frames using the catalog-based method. We compare the performances of 3 different signal reconstruction techniques; Expectation-Based, Posterior-Based and Time-Domain reconstruction. Moreover, we compare the performance of our system with the performance of the traditional NMF model. Our method outperforms the NMF method in ASR performance and separation performance in most experimental conditions.

Gain estimation approaches in catalog-based single-channel speech-music separation

“…Therefore, after some iterations with the original posterior update Equation 11, the frames with the MAP can be chosen as the active frames. Then the posterior probability of these MAP frames are assigned to 1 so as to estimate the gain parameter more accurately.…”

“…In the previous study [11], it was shown that the overall joint posterior distribution over hidden sources (speech, music sources and catalog indexes) is a mixture of multinomials. For each j, the posterior distribution of the latent sources is a multinomial distribution as follows M(s j u1t , .., s j uBt , m j ut ; X ut , p j u1t , ..., p j uBt , p j ut )…”

“…As a result, the hyper-parameters of the speech and music signals can be updated using the EM algorithm which is described in [11] in detail. These update equations correspond to the multiplicative update rules of the NMF method.…”

“…In a previous study [11], [12], [13] , we introduced a simple probabilistic model-based approach to separate speech from music. Unlike other probabilistic approaches, we do not model the speech in great detail, but instead focus on a model for the music.…”

“…Unlike the previous studies [11], [12], we address the gain estimation problem of the catalog-based method and propose three different techniques to enhance the gain estimation performance of the catalog-based method. With the analysis of the problem, we decided to use Gamma Markov Chain (GMC) probabilistic structure to impose the correlation between the gain parameters across the time frames.…”