  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.
