Monaural Speech Separation Based on a 2D Processing and Harmonic Analysis

摘要

This paper proposes a new Computational Auditory Scene Analysis (CASA) approach based on a 2D spectro-temporal analysis and harmonic separation. The 2D processing, so-called Grating Compression Transform (GCT), analyzes the spectro-temporal content of the spectrogram, mimicking the processing of the primary auditory cortex. The estimated pitches from the GCT analysis are used for separation using harmonic magnitude suppression (HMS). A powerful aspect of our model is requiring no prior training on a specific training corpus. A baseline system based on the harmonic separation is designed for comparison. Since the baseline system is similar to the proposed except the auditory-cortex-like analysis, the SIR results illustrate its importance in this task.