Humans have the ability to easily separate a composed speech and to form perceptual representations of the constituent sources in an acoustic mixture thanks to their ears. Until recently, researchers attempt to build computer models of high-level functions of the auditory system. The problem of the composed speech segregation is still a very challenging problem for these researchers. In our case, we are interested in approaches that are addressed to the monaural speech segregation. For this purpose, we study in this paper the computational auditory scene analysis (CASA) to segregate speech from monaural mixtures. CASA is the reproduction of the source organization achieved by listeners. It is based on two main stages: segmentation and grouping. In this work, we have presented, and compared several studies that have used CASA for speech separation and recognition.
In this paper, we present a method for separating voiced sounds from a composite signal. This method is mainly based on the separation by modified comb filter. This filter is keyed to the average values of the estimated pitch. This estimation is performed through an autocorrelation of multi-scale product analysis to separate the effects of the source and the vocal tract. The "autocorrelation of the multi-scale product" method allows noise elimination and the apparition of a signal periodic structure. Peaks that appear are used to calculate the mean fundamental frequency of the target speaker which will be used in the corresponding comb filters to determine the target speaker contribution. After the subtraction of this contribution from the mixture, we obtain the intrusion speaker. This separation is validated by its application on Cooke database and a part of VCTK database and compared to recent methods as Wang-Brown, Hu-Wang, Zhang-Liu and Quan systems. Results confirm the performance of the proposed approach.
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