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In this paper, we propose a new supervised monaural source separation based on autoencoders. We employ the autoencoder for the dictionary training such that the nonlinear network can encode the target source with high expressiveness. The dictionary is trained by each target source without the mixture signal, which makes the system independent from the context where the dictionaries will be used. In...
Effective denoising of noise-corrupted speech signals remains a challenging problem. Existing solutions typically employ some combination of noise estimation and noise elimination, either by subtraction or by filtering. The estimation of noise and the denoising are generally treated as independent aspects of the problem. In this paper we propose a new neural-network-based approach for de-noising of...
We propose a new algorithm for blind source separation (BSS) approach that combines independent component analysis (ICA) and frequency subband beamforming interpolation. The slow convergence of the optimization of the separation filters is a problem in ICA. Our approach to resolve this problem is based on the relationship between ICA and null beamforming (NBF). The proposed method consists of the...
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