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The sources separated by most single channel audio source separationtechniques are usually distorted and each separated source contains residualsignals from the other sources. To tackle this problem, we propose to enhancethe separated sources to decrease the distortion and interference between theseparated sources using deep neural networks (DNNs). Two different DNNs areused in this work. The first DNN is used to separate the sources from the mixedsignal. The second DNN is used to enhance the separated signals. To considerthe interactions between the separated sources, we propose to use a single DNNto enhance all the separated sources together. To reduce the residual signalsof one source from the other separated sources (interference), we train the DNNfor enhancement discriminatively to maximize the dissimilarity between thepredicted sources. The experimental results show that using discriminativeenhancement decreases the distortion and interference between the separatedsources.

Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks