Speech Dereverberation Based on Probabilistic Models of Source and Room Acoustics

This paper proposes a new single channel speech dereverberation method, in which the features of source signals and room acoustics are represented by probabilistic density functions (pdf) and the source signals are estimated by maximizing a likelihood function defined based on the pdfs. Two types of pdfs are introduced for the source signals, based on two essential speech signal features, harmonicity and sparseness, while the pdf for the room acoustics is defined based on an inverse filtering operation. The EM algorithm is used to solve this maximum likelihood problem efficiently. The resultant algorithm elaborates the initial source signal estimate given solely based on its source signal features by integrating them with the room acoustics feature through the EM iteration. The effectiveness of the present method is shown in terms of the energy decay curves of the dereverberated impulse responses