Robust signal-to-noise ratio estimation based on waveform amplitude distribution analysis

In this paper, we introduce a new algorithm for estimating the signal-to-noise ratio (SNR) of speech signals, called WADASNR (Waveform Amplitude Distribution Analysis). In this algorithm we assume that the amplitude distribution of clean speech can be approximated by the Gamma distribution with a shaping parameter of 0.4, and that an additive noise signal is Gaussian. Based on this assumption, we can estimate the SNR by examining the amplitude distribution of the noisecorrupted speech. We evaluate the performance of the WADA-SNR algorithm on databases corrupted by white noise, background music, and interfering speech. The WADA-SNR algorithm shows significantly less bias and less variability with respect to the type of noise compared to the standard NIST STNR algorithm. In addition, the algorithm is quite computationally efficient.