# Task splitting for DNN-based acoustic echo and noise removal

arXiv: https://arxiv.org/abs/2205.06931 · HTML: https://mlsanity.com/paper/2205.06931/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2022.

## Built on by (most cited first, 2 of 2)

- [Model as Loss: A Self-Consistent Training Paradigm](https://mlsanity.com/paper/2505.21156/) (2025)
- [Knowledge Distillation for Efficient Acoustic Echo Control](https://mlsanity.com/paper/2608.25596/) (2026)

## Abstract (arXiv)

Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC). In this work, we evaluate whether it is more useful to use a single joint or separate modules to tackle these problems. We describe different possible implementations and give insights into their performance and efficiency. We show that using a separate echo cancellation module and a module for noise and residual echo removal results in less near-end speech distortion and better performance during double-talk at same complexity.