Audio-Visual Speech Enhancement In Complex Scenarios With Separation And Dereverberation Joint Modeling

• URL: http://arxiv.org/abs/2510.26825v1
• Date: Wed, 29 Oct 2025 03:08:55 GMT
• Title: Audio-Visual Speech Enhancement In Complex Scenarios With Separation And Dereverberation Joint Modeling
• Authors: Jiarong Du, Zhan Jin, Peijun Yang, Juan Liu, Zhuo Li, Xin Liu, Ming Li,
• Abstract summary: We propose an effective AVSE system that performs well in complex acoustic environments.<n> Specifically, we design a "separation before dereverberation" pipeline that can be extended to other AVSE networks.
• Score: 10.757994012708734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio-visual speech enhancement (AVSE) is a task that uses visual auxiliary information to extract a target speaker's speech from mixed audio. In real-world scenarios, there often exist complex acoustic environments, accompanied by various interfering sounds and reverberation. Most previous methods struggle to cope with such complex conditions, resulting in poor perceptual quality of the extracted speech. In this paper, we propose an effective AVSE system that performs well in complex acoustic environments. Specifically, we design a "separation before dereverberation" pipeline that can be extended to other AVSE networks. The 4th COGMHEAR Audio-Visual Speech Enhancement Challenge (AVSEC) aims to explore new approaches to speech processing in multimodal complex environments. We validated the performance of our system in AVSEC-4: we achieved excellent results in the three objective metrics on the competition leaderboard, and ultimately secured first place in the human subjective listening test.

Related papers

• Real-Time Audio-Visual Speech Enhancement Using Pre-trained Visual Representations [5.130705720747573]
  This paper presents a real-time audio-visual speech enhancement (AVSE) system, RAVEN.<n>It isolates and enhances the on-screen target speaker while suppressing interfering speakers and background noise.<n>To our knowledge, this is the first open-source implementation of a real-time AVSE system.
  arXiv  Detail & Related papers  (2025-07-29T02:38:56Z)
• Cocktail-Party Audio-Visual Speech Recognition [58.222892601847924]
  This study introduces a novel audio-visual cocktail-party dataset designed to benchmark current AVSR systems.<n>We contribute a 1526-hour AVSR dataset comprising both talking-face and silent-face segments, enabling significant performance gains in cocktail-party environments.<n>Our approach reduces WER by 67% relative to the state-of-the-art, reducing WER from 119% to 39.2% in extreme noise, without relying on explicit segmentation cues.
  arXiv  Detail & Related papers  (2025-06-02T19:07:51Z)
• Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM [53.17360668423001]
  Overlapping Speech Detection (OSD) aims to identify regions where multiple speakers overlap in a conversation.<n>This work proposes a speaker-aware progressive OSD model that leverages a progressive training strategy to enhance the correlation between subtasks.<n> Experimental results show that the proposed method achieves state-of-the-art performance, with an F1 score of 82.76% on the AMI test set.
  arXiv  Detail & Related papers  (2025-05-29T07:47:48Z)
• Multi-Domain Audio Question Answering Toward Acoustic Content Reasoning in The DCASE 2025 Challenge [102.84031769492708]
  This task defines three QA subsets to test audio-language models on interactive question-answering over diverse acoustic scenes.<n>Preliminary results on the development set are compared, showing strong variation across models and subsets.<n>This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity.
  arXiv  Detail & Related papers  (2025-05-12T09:04:16Z)
• Stepping Stones: A Progressive Training Strategy for Audio-Visual Semantic Segmentation [7.124066540020968]
  Audio-Visual (AVS) aims to achieve pixel-level localization of sound sources in videos, while Audio-Visual Semantic (AVSS) pursues semantic understanding of audio-visual scenes. Previous methods have struggled to handle this mashup of objectives in end-to-end training, resulting in insufficient learning and sub-optimization. We propose a two-stage training strategy called textitStepping Stones, which decomposes the AVSS task into two simple subtasks from localization to semantic understanding, which are fully optimized in each stage to achieve step-by-step global optimization
  arXiv  Detail & Related papers  (2024-07-16T15:08:30Z)
• Bootstrapping Audio-Visual Segmentation by Strengthening Audio Cues [75.73217916395386]
  We propose a Bidirectional Audio-Visual Decoder (BAVD) with integrated bidirectional bridges. This interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations. We also present a strategy for audio-visual frame-wise synchrony as fine-grained guidance of BAVD.
  arXiv  Detail & Related papers  (2024-02-04T03:02:35Z)
• MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition [62.89464258519723]
  We propose a multi-layer cross-attention fusion based AVSR approach that promotes representation of each modality by fusing them at different levels of audio/visual encoders. Our proposed approach surpasses the first-place system, establishing a new SOTA cpCER of 29.13% on this dataset.
  arXiv  Detail & Related papers  (2024-01-07T08:59:32Z)
• AdVerb: Visually Guided Audio Dereverberation [49.958724234969445]
  We present AdVerb, a novel audio-visual dereverberation framework. It uses visual cues in addition to the reverberant sound to estimate clean audio.
  arXiv  Detail & Related papers  (2023-08-23T18:20:59Z)
• Joint Speech Recognition and Audio Captioning [37.205642807313545]
  Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. We aim to bring together the growing field of automated audio captioning (AAC) and the thoroughly studied automatic speech recognition (ASR) We propose several approaches for end-to-end joint modeling of ASR and AAC tasks.
  arXiv  Detail & Related papers  (2022-02-03T04:42:43Z)
• Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks [20.316239155843963]
  We propose a self-supervised audio representation learning method and apply it to a variety of downstream non-speech audio tasks. On the AudioSet benchmark, we achieve a mean average precision (mAP) score of 0.415, which is a new state-of-the-art on this dataset.
  arXiv  Detail & Related papers  (2021-10-14T12:32:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.