Blind Channel Magnitude Response Estimation in Speech Using Spectrum Classification

Abstract

We present an algorithm for blind estimation of the magnitude response of an acoustic channel from single microphone observations of a speech signal. The algorithm employs channel robust RASTA filtered Mel-frequency cepstral coefficients as features to train a Gaussian mixture model based classifier and average clean speech spectra are associated with each mixture; these are then used to blindly estimate the acoustic channel magnitude response from speech that has undergone spectral modification due to the channel. Experimental results using a variety of simulated and measured acoustic channels and additive babble noise, car noise and white Gaussian noise are presented. The results demonstrate that the proposed method is able to estimate a variety of channel magnitude responses to within an Itakura distance of dI ≤0.5 for SNR ≥10 dB.