  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.
