Monaural Speech Separation on Many Integrated Core Architecture

Abstract

Monaural speech separation is a challenging problem in practical audio analysis applications. Non-negative matrix factorization (NMF) is one of the most effective methods to solve this problem because it can learn meaningful features from a speech dataset in a supervised manner. Recently, a semi-supervised method, i.e., transductive NMF (TNMF), has shown great power to separate speeches from different individuals by incorporating both training and testing data in learning the dictionary. However, both NMF-based and TNMF-based monaural speech separation approaches have high computational complexity, and prohibit them from real-time processing. In this paper, we implement TNMF-based monaural speech separation on many integrated core (MIC) architecture to meet the requirement of real-time speech separation. This approach conducts parallelism based on the OpenMP technology, and performs the computing intensitive matrix manipulations on a MIC coprocessor. The experimental results confirm the efficiency of our implementation of monaural speech separation on MIC architecture.