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We describe a modulation-domain loss function for deep-learning-based speechenhancement systems. Learnable spectro-temporal receptive fields (STRFs) wereadapted to optimize for a speaker identification task. The learned STRFs werethen used to calculate a weighted mean-squared error (MSE) in the modulationdomain for training a speech enhancement system. Experiments showed that addingthe modulation-domain MSE to the MSE in the spectro-temporal domainsubstantially improved the objective prediction of speech quality andintelligibility for real-time speech enhancement systems without incurringadditional computation during inference.

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