Performance comparison of real-time single-channel speech dereverberation algorithms

This paper investigates four single-channel speech dereverberation algorithms, i.e., two unsupervised approaches based on (i) spectral enhancement and (ii) linear prediction, as well as two supervised approaches relying on machine learning which incorporate deep neural networks to predict either (iii) the magnitude spectrogram or (iv) the ideal ratio mask. The relative merits of the four algorithms in terms of several objective measures, automatic speech recognition performance, robustness against noise, variations between simulated and recorded reverberant speech, computation time and latency are discussed. Experimental results show that all four algorithms are capable of providing benefits in reverberant environments even with moderate background noises. In addition, low complexity and latency indicate their potential for real-time applications.

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