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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...
Large-scale distributed learning plays an ever-more increasing role in modern computing. However, whether using a compute cluster with thousands of nodes, or a single multi-GPU machine, the most significant bottleneck is that of communication. In this work, we explore the effects of applying quantization and encoding to the parameters of distributed models. We show that, for a neural network, this...
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