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Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks
In recent decades, neural network based methods have significantly impro...
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WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
Due to the simple design pipeline, end-to-end (E2E) neural models for sp...
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Efficient Context Aggregation for End-to-End Speech Enhancement Using a Densely Connected Convolutional and Recurrent Network
In speech enhancement, an end-to-end deep neural network converts a nois...
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Using recurrences in time and frequency within U-net architecture for speech enhancement
When designing fully-convolutional neural network, there is a trade-off ...
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Towards end-to-end speech enhancement with a variational U-Net architecture
In this paper, we investigate the viability of a variational U-Net archi...
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Speech Enhancement with Wide Residual Networks in Reverberant Environments
This paper proposes a speech enhancement method which exploits the high ...
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Convolutional-Recurrent Neural Networks for Speech Enhancement
We propose an end-to-end model based on convolutional and recurrent neur...
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RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn long-range temporal correlations across high-resolution waveforms. These models, however, are limited by memory-intensive dilated convolution and aliasing artifacts from upsampling. We introduce an end-to-end fully-recurrent hourglass-shaped neural network architecture with residual connections for waveform-based single-channel speech enhancement. Our model can efficiently capture long-range temporal dependencies by reducing the features resolution without information loss. Experimental results show that our model outperforms state-of-the-art approaches in six evaluation metrics.
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