# DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation

arXiv: https://arxiv.org/abs/2306.03177 · HTML: https://mlsanity.com/paper/2306.03177/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [ICASSP 2022 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2202.13288/) (2022)
- [Towards efficient models for real-time deep noise suppression](https://mlsanity.com/paper/2101.09249/) (2021)
- [FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement](https://mlsanity.com/paper/2203.12188/) (2022)
- [Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filters](https://mlsanity.com/paper/1904.08369/) (2019)
- [Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression](https://mlsanity.com/paper/2005.07551/) (2020)
- [Interactive Speech and Noise Modeling for Speech Enhancement](https://mlsanity.com/paper/2012.09408/) (2020)
- [AECMOS: A speech quality assessment metric for echo impairment](https://mlsanity.com/paper/2110.03010/) (2021)
- [Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet](https://mlsanity.com/paper/2111.03847/) (2021)
- [ICASSP 2022 Acoustic Echo Cancellation Challenge](https://mlsanity.com/paper/2202.13290/) (2022)
- [NN3A: Neural Network supported Acoustic Echo Cancellation, Noise Suppression and Automatic Gain Control for Real-Time Communications](https://mlsanity.com/paper/2110.08437/) (2021)
- [Multi-Task Deep Residual Echo Suppression with Echo-aware Loss](https://mlsanity.com/paper/2202.06850/) (2022)
- [EchoFilter: End-to-End Neural Network for Acoustic Echo Cancellation](https://mlsanity.com/paper/2105.14666/) (2021)
- [Deep model with built-in cross-attention alignment for acoustic echo cancellation](https://mlsanity.com/paper/2208.11308/) (2022)
- [SCA: Streaming Cross-attention Alignment for Echo Cancellation](https://mlsanity.com/paper/2211.00589/) (2022)

## Built on by (most cited first, 6 of 6)

- [LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement](https://mlsanity.com/paper/2503.00493/) (2025)
- [Boosting keyword spotting through on-device learnable user speech characteristics](https://mlsanity.com/paper/2403.07802/) (2024)
- [UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling](https://mlsanity.com/paper/2508.07558/) (2025)
- [Personalized Speech Enhancement Without a Separate Speaker Embedding Model](https://mlsanity.com/paper/2406.09928/) (2024)
- [Knowledge Distillation for Efficient Acoustic Echo Control](https://mlsanity.com/paper/2608.25596/) (2026)
- [Adapting Personalized Speech Enhancement for Low-Latency Audio-Visual Target-Speaker Extraction](https://mlsanity.com/paper/2609.30631/) (2026)

## Abstract (arXiv)

Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation (DR) are an integral part of modern full-duplex communication systems. As the demand for teleconferencing systems increases, addressing these tasks is required for an effective and efficient online meeting experience. Most prior research proposes solutions for these tasks separately, combining them with digital signal processing (DSP) based components, resulting in complex pipelines that are often impractical to deploy in real-world applications. This paper proposes a real-time cross-attention deep model, named DeepVQE, based on residual convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to simultaneously address AEC, NS, and DR. We conduct several ablation studies to analyze the contributions of different components of our model to the overall performance. DeepVQE achieves state-of-the-art performance on non-personalized tracks from the ICASSP 2023 Acoustic Echo Cancellation Challenge and ICASSP 2023 Deep Noise Suppression Challenge test sets, showing that a single model can handle multiple tasks with excellent performance. Moreover, the model runs in real-time and has been successfully tested for the Microsoft Teams platform.