We present a single channel method for late reverberation suppression. The proposed approach estimates late reverberation as a linear combination of previous time-frequency frames. We impose a sparsity constraint on the predictor in order to select the most relevant signal frames for the estimation. The dataset used for the evaluation is corrupted by background noise, thus we propose to jointly suppress background noise and late reverberation. This leads to an important improvement in the quality of the processed signals as well as an improvement of the automatic speech recognition scores. The method appears to be efficient mainly in far field conditions and in highly reverberant environments. In addition, it is suitable for real time processing.

Reverberation degrades speech intelligibility in telecommunications as well as it increases the word error rate in automatic speech recognition tasks. Several dereverberation methods have been proposed recently in order to counter these effects. In the single microphone case, the dereverberation problem is underdetermined and reverberation suppression approaches are preferred. In this paper we propose a novel method for single channel reverberation suppression. Late reverberation is estimated in the time-frequency domain as a sparse linear combination of previous frames. The predictors associated to the model are determined in a Lasso framework and a spectral subtraction filter is designed to produce the enhanced signal. This model does not require any additional information about the room acoustics and it is well suited for real-time applications. The method has state-of-the-art performance in terms of both reverberation suppression and spectral distortion.

Multichannel blind source separation performances rapidly degrade when the mixtures are highly reverberated. In fact, blind source separation algorithms usually focus on the separation task without dealing with the dereverberation problem. Some recent studies attempted to reduce the reverberation by introducing a dereverberation module before or after the blind source separation but only limited success was obtained in improving the separation performance in highly reverberant rooms. In this article, we conduct a number of experiments combining state of the art spectral enhancement- based dereverberation and source separation algorithms showing that, in this particular case, speech enhancement does not improve the performance of blind source separation.

The reverberation time is a key feature for describing the acoustic properties of a reverberant room. It can be computed from a measured Room Impulse Response but in many applications it has to be estimated blindly. Existing blind methods give accurate estimates but they often exhibit high variance across different speakers. In this paper, a low variance blind estimator of the reverberation time is derived from the decay rate distribution of the signal. The influence of the reverberation time on the statistical moments of the distribution is analyzed and one relevant moment is taken as an estimator. The variance of the estimator is reduced thanks to a prewhitening filter and a modification of the decay rate distribution. Experimental results confirm the accuracy of the method when the observed signal is sufficiently long.