A Simple and Effective Framework for a Priori SNR Estimation

Abstract

The problem of estimating the a priori signal-to-noise ratio (SNR) for single-channel speech enhancement is addressed. Similar to the decision-directed approach we linearly combine the maximum likelihood estimate of the a priori SNR with an estimate obtained from the previous frame. Based on the harmonic model for voiced speech we propose to smooth the a priori SNR estimate along harmonic trajectories instead of fixed discrete Fourier transform frequency bins. We interpolate by using a pitch-adaptive zero-padding in order to obtain the spectral coefficients at harmonic frequencies. The resulting pitch-adaptive decision-directed (PADDi) method increases the noise attenuation compared to the classical decision-directed approach and outperforms benchmark methods in terms of speech enhancement performance for several noise types at different SNRs, Quantified by objective evaluation criteria.