Speech Denoising with Deep Feature Losses.
Francois G. Germain,Qifeng Chen,Vladlen Koltun +2 more
- pp 2723-2727
TLDR
In this article, a fully-convolutional context aggregation network using a deep feature loss is proposed to denoise speech signals by processing the raw waveform directly, which achieves state-of-the-art performance in objective speech quality metrics and in large-scale perceptual experiments with human listeners.Abstract:
We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly. Given input audio containing speech corrupted by an additive background signal, the system aims to produce a processed signal that contains only the speech content. Recent approaches have shown promising results using various deep network architectures. In this paper, we propose to train a fully-convolutional context aggregation network using a deep feature loss. That loss is based on comparing the internal feature activations in a different network, trained for acoustic environment detection and domestic audio tagging. Our approach outperforms the state-of-the-art in objective speech quality metrics and in large-scale perceptual experiments with human listeners. It also outperforms an identical network trained using traditional regression losses. The advantage of the new approach is particularly pronounced for the hardest data with the most intrusive background noise, for which denoising is most needed and most challenging.read more
Citations
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MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement.
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Real Time Speech Enhancement in the Waveform Domain.
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PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
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Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement
TL;DR: This paper investigates several aspects of training a RNN (recurrent neural network) that impact the objective and subjective quality of enhanced speech for real-time single-channel speech enhancement and proposes two novel mean-squared-error-based learning objectives.
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