Harmonicity based blind dereverberation with time warping

Speech dereverberation is desirable in applications such as robust automatic speech recognition (ASR) in the real world. Although a number of dereverberation methods have been exploited, dereverberation is still a challenging problem especially when using a single microphone. To overcome this problem, a harmonicity based dereverberation method (HERB) has recently been proposed. HERB can blindly estimate the inverse filter of a room impulse response based on harmonicity of speech signals and dereverberate the signals. However, HERB uses an imprecise assumption that hinders the dereverberation performance, that is, the fundamental frequency ( ) of a speech signal is assumed to be constant within a short time frame when extracting the features of harmonic components. In this paper, we introduce time warping analysis into HERB to remove this bottleneck. Time warping analysis expands and contracts the time axis of a signal in order to make the of the signal constant, and makes it possible to estimate harmonic components precisely even when their frequencies change rapidly. We show that time warping analysis can effectively improve the dereverberation effect of HERB when the reverberation time is longer than 0.1 sec.