# Knowledge Distillation for Efficient Acoustic Echo Control

arXiv: https://arxiv.org/abs/2608.25596 · HTML: https://mlsanity.com/paper/2608.25596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speech and audio distillation, one of 12 directions in Knowledge distillation.
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Placed in

- Knowledge distillation / [Speech and audio distillation](https://mlsanity.com/distill/speech-and-audio-distillation/)

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation](https://mlsanity.com/paper/2306.10687/) (2023)
- [Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation](https://mlsanity.com/paper/2105.03544/) (2021)
- [Two-Step Knowledge Distillation for Tiny Speech Enhancement](https://mlsanity.com/paper/2309.08144/) (2023)
- [AECMOS: A speech quality assessment metric for echo impairment](https://mlsanity.com/paper/2110.03010/) (2021)
- [DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation](https://mlsanity.com/paper/2306.03177/) (2023)
- [Crowdsourcing approach for subjective evaluation of echo impairment](https://mlsanity.com/paper/2010.13063/) (2020)
- [Y2-Net FCRN for Acoustic Echo and Noise Suppression](https://mlsanity.com/paper/2103.17189/) (2021)
- [Task splitting for DNN-based acoustic echo and noise removal](https://mlsanity.com/paper/2205.06931/) (2022)
- [Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering](https://mlsanity.com/paper/2207.11388/) (2022)
- [NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation](https://mlsanity.com/paper/2301.12363/) (2023)
- [End-To-End Deep Learning-based Adaptation Control for Linear Acoustic Echo Cancellation](https://mlsanity.com/paper/2306.02450/) (2023)
- [Efficient Acoustic Echo Suppression with Condition-Aware Training](https://mlsanity.com/paper/2307.15630/) (2023)
- [Neural Kalman Filters for Acoustic Echo Cancellation](https://mlsanity.com/paper/2501.16367/) (2025)

## Abstract (arXiv)

In recent years, many efforts have been made to supersede classical acoustic echo control (AEC) algorithms with more powerful machine-learned approaches. While surpassing the performance of well-established adaptive filters is very much possible, a remaining challenge is computational complexity. Popular architectures, such as convolutional recurrent networks (CRNs), are by multiple orders of magnitude computationally more expensive than classical signal processing solutions. Scaling down such models is usually straight-forward, but it comes at the cost of a notably reduced performance. We show - to the author's knowledge for the first time in AEC - how these performance drops can be successfully alleviated to a large degree by employing an effective knowledge distillation (KD) process, enabling more potent efficient AEC. Our proposed CGGN16 student AEC models show significantly less near-end speech distortion at only 2% of its teacher's computational complexity, surpass the overall performance of a six times more complex model trained on ground-truth labels, and outperform other AEC-focused architectures from recent literature.