Fast noise PSD estimation based on blind channel identification

This paper introduces a new binaural noise estimator based on the target cancellation technique. The left and right source-to-microphone transfer functions (channels) are blindly estimated by means of the constrained least-mean-square algorithm, minimizing the cross-relation error between left and right microphone signals. This blind channel identification (BCI) thus implies a blocking of the target signal and a biased noise estimation in the error signal. The related noise power is then corrected using the estimated channels. The performance of the proposed algorithm is investigated in comparison to different single and dual channel noise power estimators. The investigations show that the proposed algorithm is capable of estimating and tracking the noise power fast and accurately. The suitability of the noise power spectral density (PSD) estimator is finally confirmed within a speech enhancement framework.