Multi-branch Learning for Noisy and Reverberant Monaural Speech Separation

With the rapid development of deep learning approaches, much progress has been made on speech enhancement, speech dereverberation, and monaural multi- speaker speech separation to solve the cocktail problem. Some excellent methods have been proposed to solve the monaural speech separation in a noisy and reverberant environment. However, few studies exploit the correlations between anechoic speech and reverberant speech. In this work, the structure of a popular separation system is deconstructed, and a multi-branch learning method is proposed to enforce the network to exploit the correlations between anechoic speech and the corresponding reverberant speech. The results show that using multi-branch learning can improve the separation performance of different networks by 0.7dB with conv-tasnet on the WHAMR! dataset.