Auditory filter-bank compression improves estimation of signal-to-noise ratio for speech in noise

Signal-to-noise ratio (SNR) estimation is necessary for many speech processing applications often challenged by nonstationary noise. The authors have previously demonstrated that the variance of spectral entropy (VSE) is a reliable estimate of SNR in nonstationary noise. Based on pre-estimated VSE-SNR relationship functions, the SNR of unseen acoustic environments can be estimated from the measured VSE. This study predicts that introducing a compressive function based on cochlear processing will increase the stability of the pre-estimated VSE-SNR relationship functions. This study demonstrates that calculating the VSE based on a nonlinear filter-bank, simulating cochlear compression, reduces the VSE-based SNR estimation errors. VSE-SNR relationship functions were estimated using speech tokens presented in babble noise comprised of different numbers of speakers. Results showed that the coefficient of determination (R²) of the estimated VSE-SNR relationship functions have absolute percentage improvements of over 26% when using a filter-bank with a compressive function, compared to when using a linear filter-bank without compression. In 2-talker babble noise, the estimation accuracy is more than 3 dB better than other published methods.