Abstract
The estimation accuracy of the late reverberation power spectral density (PSD) is of paramount importance in single-channel frequency-domain dereverberation algorithms. In this domain, the reverberant signal can be modeled by the convolution of an early speech component and a relative convolutive transfer function (RCTF). In this work, the RCTF coefficients are modeled by a first-order Markov chain, which is well-suited to model time-varying scenarios. The RCTF coefficients are estimated online by a Kalman filter and are then used to compute the late reverberation PSD, which is used in a spectral enhancement filter to achieve dereverberation and noise reduction. It is shown that the proposed reverberation PSD estimator yields similar performance to other estimators, which impose a model on the reverberant tail and which depend on additional information like the reverberation time and the direct-to-reverberation ratio.