Bayesian periodogram smoothing for speech enhancement

Periodogram smoothing of the received noisy signal is a challenging problem in speech enhancement. We present a Bayesian approach, where the instantaneous periodogram is smoothed through an adaptive smoothing parameter. By updating sufficient statistics using new samples of the noisy signal, the smoothing parameter is adjusted on-line. The performance of the novel smoothing algorithm is studied in a speech enhancement context. It is demonstrated that with respect to Mean Square Error, the proposed Bayesian smoothing algorithm performs better than the other non-Bayesian smoothing algorithms in higher signal-to-noise ratio environments.