# Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression

arXiv: https://arxiv.org/abs/2106.13531 · HTML: https://mlsanity.com/paper/2106.13531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets, Testing Framework, and Results](https://mlsanity.com/paper/2009.04972/) (2020)
- [Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network](https://mlsanity.com/paper/2005.09237/) (2020)
- [Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise](https://mlsanity.com/paper/1911.08934/) (2019)

## Built on by (most cited first, 1 of 1)

- [End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression](https://mlsanity.com/paper/2110.00745/) (2021)

## Abstract (arXiv)

In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. This system embeds a design parameter that allows a tunable tradeoff between the desired-signal distortion and residual echo suppression in double-talk scenarios. The system employs 136 thousand parameters, and requires 1.6 Giga floating-point operations per second and 10 Mega-bytes of memory. The implementation satisfies both the timing requirements of the AEC challenge and the computational and memory limitations of on-device applications. Experiments are conducted with 161~h of data from the AEC challenge database and from real independent recordings. We demonstrate the performance of the proposed system in real-life conditions and compare it with two competing methods regarding echo suppression and desired-signal distortion, generalization to various environments, and robustness to high echo levels.