A Comparison of Non-Intrusive SNR Estimation Algorithms and the Use of Mapping Functions

Abstract

We present a comparative evaluation of six methods for non-intrusive Signal-to-Noise Ratio (SNR) estimation for narrowband speech in noise. We demonstrate that the performance of all methods can be improved by applying a non-linear mapping function to their estimates of SNR. We have employed phrases built from the TIMIT speech corpus and noises from a broad range of sources including ITU-T P.501, NOISEX-92, and Soundjay. We compare the accuracy of the methods in estimating the SNR of both stationary and non-stationary noise and we conclude that with the mapping function, the best current methods can estimate the SNR to within approximately 3.5 dB for SNRs from -5 dB to 35 dB.