Variable sparsity regularization factor based SNMF for monaural speech separation
Yash Vardhan Varshney, Zia Ahmad Abbasi, Musiur Raza Abidi, Omar Farooq · 2017
Factor of sparsity in a speech signal plays an important role in the speech processing. This paper proposed a method in which variable regularization factor of sparsity is applied for the mixed signal and used to separate the monaural speech signals. The sparsity regularization factor for individual training and testing signal was find using particle swarm optimization. Algorithm has been tested for speech-speech separation using TIMIT database and music-speech separation using MIR -1K database. Results are evaluated for fixed and variable regularization factor of sparsity in the cases of both speech-speech and music-speech mixed signal separation. Proposed model has been compared with the existing model based on fixed sparsity factor and has shown superior performance in terms of SDR, SIR and other objective measures.