It is optimized on both time and frequency domains, using multiple loss functions.Įmpirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb.Īdditionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. The proposed model is based on an encoder-decoder architecture with skip-connections. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. We provide a PyTorch implementation of the paper: Real Time Speech Enhancement in the Waveform Domain.
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)
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