Boosting Spatial Information for Deep Learning Based Multichannel Speaker-Independent Speech Separation In Reverberant Environments
Ziye Yang, Xiao-Lei Zhang · 2019
Recently, supervised speaker-independent speech separation methods, such as deep clustering and permutation invariant training, have demonstrated better performance than conventional unsupervised speech separation methods. However, their performance drops sharply in reverberant environments. To solve the problem, we propose a multi-channel speech separation algorithm that fully explores spatial information. It first extracts a spatial feature, named interaural phase difference (IPD), as one of the input features of the single-channel deep clustering algorithm. Then, it uses the deep clustering as the noise estimation component of the deep-learning-based beamforming. The novelty of the proposed algorithm lies in that it extends the spatial-feature-based deep clustering to a multichannel algorithm which boosts the performance by exploring spatial information at both the input and output of deep clustering. Its advantages have two aspects. First, the spatial feature IPD significantly improves the robustness of deep clustering in reverberant environments. Second, the deep-clusteing-based beamforming, which is a linear algorithm, suffers less nonlinear distortions than the single-channel deep clustering. We have compared the proposed algorithm with the single-channel deep clustering algorithm, spatial-feature-based multi-channel deep clustering with IPD, and deep-clustering-based beamforming without IPD in reverberant environments. Experimental results show that the proposed algorithm performs significantly better than the comparison methods.