Speech Noise Estimation using Enhanced Minima Controlled Recursive Averaging

Accurate noise power spectrum estimation in a noisy speech signal is a key challenge problem in speech enhancement. One state-of-the-art approach is the minima controlled recursive averaging (MCRA). This paper presents an enhanced MCRA algorithm (EMCRA), which demonstrates less speech signal leakage and faster response time to follow abrupt changes in the noise power spectrum. Experiments using real speech and noise recordings have validated the superiority of the proposed enhancements. EMCRA shows improvements both in intuitive subjective listening and objective quality measures in terms of higher output SNR and lower output distortion scores.