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Binaural features of interaural level difference and interaural phase difference have proved to be very effective in training deep neural networks (DNNs), to generate time-frequency masks for target speech extraction in speech-speech mixtures. However, effectiveness of binaural features is reduced in more common speech-noise scenarios, since the noise may over-shadow the speech in adverse conditions...
In this paper, a novel deep neural network (DNN) architecture is proposed to generate the speech features of both the target speaker and interferer for speech separation without using any prior information about the interfering speaker. DNN is adopted here to directly model the highly nonlinear relationship between speech features of the mixed signals and the two competing speakers. Experimental results...
This paper proposes a novel data-driven approach based on deep neural networks (DNNs) for single-channel speech separation. DNN is adopted to directly model the highly non-linear relationship of speech features between a target speaker and the mixed signals. Both supervised and semi-supervised scenarios are investigated. In the supervised mode, both identities of the target speaker and the interfering...
This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be...
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