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.
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