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A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement

2021-02-15Code Available1· sign in to hype

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Abstract

We describe a modulation-domain loss function for deep-learning-based speech enhancement systems. Learnable spectro-temporal receptive fields (STRFs) were adapted to optimize for a speaker identification task. The learned STRFs were then used to calculate a weighted mean-squared error (MSE) in the modulation domain for training a speech enhancement system. Experiments showed that adding the modulation-domain MSE to the MSE in the spectro-temporal domain substantially improved the objective prediction of speech quality and intelligibility for real-time speech enhancement systems without incurring additional computation during inference.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Deep Noise Suppression (DNS) ChallengeRNN-ModulationPESQ-WB2.75Unverified
DNS ChallengeRNN-ModulationPESQ-WB2.75Unverified
VoiceBank + DEMANDreal-time-GRUPESQ (wb)2.82Unverified

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