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

arXiv: https://arxiv.org/abs/2102.07330 · HTML: https://mlsanity.com/paper/2102.07330/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds](https://mlsanity.com/paper/2210.10196/) (2022)
- [Biomimetic Frontend for Differentiable Audio Processing](https://mlsanity.com/paper/2409.08997/) (2024)
- [Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement](https://mlsanity.com/paper/2506.13127/) (2025)
- [A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions](https://mlsanity.com/paper/2508.07561/) (2025)

## Abstract (arXiv)

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.