# Efficient High-Performance Bark-Scale Neural Network for Residual Echo and Noise Suppression

arXiv: https://arxiv.org/abs/2404.11621 · HTML: https://mlsanity.com/paper/2404.11621/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [ICASSP 2023 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2303.11510/) (2023)
- [Differentiable Consistency Constraints for Improved Deep Speech Enhancement](https://mlsanity.com/paper/1811.08521/) (2018)
- [A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech](https://mlsanity.com/paper/2008.04259/) (2020)
- [Data augmentation and loss normalization for deep noise suppression](https://mlsanity.com/paper/2008.06412/) (2020)
- [AECMOS: A speech quality assessment metric for echo impairment](https://mlsanity.com/paper/2110.03010/) (2021)
- [Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based On PercepNet](https://mlsanity.com/paper/2102.05245/) (2021)
- [DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation](https://mlsanity.com/paper/2306.03177/) (2023)
- [Acoustic echo cancellation with the dual-signal transformation LSTM network](https://mlsanity.com/paper/2010.14337/) (2020)
- [Crowdsourcing approach for subjective evaluation of echo impairment](https://mlsanity.com/paper/2010.13063/) (2020)
- [Meeting Effectiveness and Inclusiveness in Remote Collaboration](https://mlsanity.com/paper/2102.09803/) (2021)
- [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)
- [SCA: Streaming Cross-attention Alignment for Echo Cancellation](https://mlsanity.com/paper/2211.00589/) (2022)
- [Deep model with built-in cross-attention alignment for acoustic echo cancellation](https://mlsanity.com/paper/2208.11308/) (2022)
- [A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation](https://mlsanity.com/paper/2012.08867/) (2020)
- [Efficient Acoustic Echo Suppression with Condition-Aware Training](https://mlsanity.com/paper/2307.15630/) (2023)
- [Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering](https://mlsanity.com/paper/2205.04276/) (2022)

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

- [DiffVQE: Hybrid Diffusion Voice Quality Enhancement Under Acoustic Echo and Noise](https://mlsanity.com/paper/2605.08189/) (2026)

## Abstract (arXiv)

In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quite demanding in terms of computational complexity and memory footprint. For the application in dedicated communication devices, such as speakerphones, hands-free car systems, or smartphones, efficiency plays a major role along with performance. In this context, we present an efficient, high-performance hybrid joint acoustic echo control and noise suppression system, whereby our main contribution is the postfilter NN, performing both noise and residual echo suppression. The preservation of nearend speech is improved by a Bark-scale auditory filterbank for the NN postfilter. The proposed hybrid method is benchmarked with state-of-the-art methods and its effectiveness is demonstrated on the ICASSP 2023 AEC Challenge blind test set. We demonstrate that it offers high-quality nearend speech preservation during both double-talk and nearend speech conditions. At the same time, it is capable of efficient removal of echo leaks, achieving a comparable performance to already small state-of-the-art models such as the end-to-end DeepVQE-S, while requiring only around 10 % of its computational complexity. This makes it easily realtime implementable on a speakerphone device.