A priori SNR Estimation Using a Generalized Decision Directed Approach

Aleksej Chinaev, Reinhold Haeb-Umbach

In this contribution we investigate a priori signal-to-noise ratio (SNR) estimation, a crucial component of a single-channel speech enhancement system based on spectral subtraction. The majority of the state-of-the art a priori SNR estimators work in the power spectral domain, which is, however, not confirmed to be the optimal domain for the estimation. Motivated by the generalized spectral subtraction rule, we show how the estimation of the a priori SNR can be formulated in the so called generalized SNR domain. This formulation allows to generalize the widely used decision directed (DD) approach. An experimental investigation with different noise types reveals the superiority of the generalized DD approach over the conventional DD approach in terms of both the mean opinion score — listening quality objective measure and the output global SNR in the medium to high input SNR regime, while we show that the power spectrum is the optimal domain for low SNR. We further develop a parameterization which adjusts the domain of estimation automatically according to the estimated input global SNR.

DOI: 10.21437/Interspeech.2016-474

Cite as

Chinaev, A., Haeb-Umbach, R. (2016) A priori SNR Estimation Using a Generalized Decision Directed Approach. Proc. Interspeech 2016, 3758-3762.

author={Aleksej Chinaev and Reinhold Haeb-Umbach},
title={A priori SNR Estimation Using a Generalized Decision Directed Approach},
booktitle={Interspeech 2016},