Real-time Single-channel Speech Enhancement with Recurrent Neural Networks
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Episode description
Single-channel speech enhancement using deep neural networks (DNNs) has shown promising progress in recent years. In this work, we explore several aspects of neural network training that impact the objective quality of enhanced speech in a real-time setting. In particular, we base all studies on a novel recurrent neural network that enhances full-band short-time speech spectra on a single-frame-in, single-frame-out basis, a framework that is adopted by most classical signal processing methods. We propose two novel learning objectives that allow separate control over expected speech distortion versus noise suppression. Moreover, we study the effect of feature normalization and sequence lengths on the objective quality of enhanced speech. Finally, we compare our method with state-of-the-art methods based on statistical signal processing and deep learning, respectively. Slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/Real-time-Single-channel-Speech-Enhancement-with-Recurrent-Neural-Networks-SLIDES.pdf Learn more about the Audio and Acoustics Research Group: https://www.microsoft.com/en-us/research/group/audio-and-acoustics-research-group/
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