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Speech Enhancement Based on Energy-Focused NMF

In this paper, we propose a novel loss function of Non-Negative Matrix Factorization (NMF) based on focusing energy on the main part. Based on this loss function, a two-steps model and a new Energy-Focused Non-Negative Matrix Factorization (EF-NMF) model are derived for speech enhancement. We use the statistical distribution to explain the energy-focused meaning and get the two different EF-NMF models inspired by the greedy algorithm. Also, IMCRA is combined to estimate the speech energy in the speech energy-focused elements. Finally, we use the IMCRA combined with EF-NMF to drive linear minimum mean square error (LMMSE) estimator. Comparing with the classical NMF that respectively trains the noise and speech basis vectors, the EF-NMF can focus more speech energy on the speech activation matrix when it decomposes noisy speech signal. The experimental results show that the EF-NMF gives better quality and intelligibility than the traditional training method of NMF, and better speech quality is gained by estimating speech elements of the matrix with IMCRA.