An algorithm combined with spectral subtraction and binary masking for monaural speech segregation

Monaural speech segregation from complex concurrent noise is an extremely challenging problem; binary mask is a method to solve this problem, however, the performance of binary mask is limited by remaining the noise in the result. In this paper, an algorithm integrated Spectral Subtraction and binary masking for speech separation and enhancement was proposed. It follows the framework of computational auditory scene analysis (CASA). The energy of time-frequency (T-F) unit was used as the clue to generate the binary mask; then the spectral subtraction algorithm was used to eliminate noise energy in original speech and an interim speech was obtained, after covered the binary mask on the interim speech, the target speech can be achieved. Systematic evaluation shows that the combined algorithm can stably improve the SNR and voice quality for noisy speech. It performs better than existing binary masking systems in most situations, especially when the noise and the speech have the similar power spectrum.