SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Model
School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Electrical Engineering and Telecommunications, The University of New South Wales（新南威尔士大学电气工程与电信学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Center for Language and Speech Processing, Johns Hopkins University（约翰霍普金斯大学语言与语音处理中心）；Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）广东省大数据计算重点实验室）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Speech enhancement plays an essential role in various applications, and the integration of visual information has been demonstrated to bring substantial advantages. However, the majority of current research concentrates on the examination of facial and lip movements, which can be compromised or entirely inaccessible in scenarios where occlusions occur or when the camera view is distant. Whereas contextual visual cues from the surrounding environment have been overlooked: for example, when we see a dog bark, our brain has the innate ability to discern and filter out the barking noise. To this end, in this paper, we introduce a novel task, i.e. SAV-SE. To our best knowledge, this is the first proposal to use rich contextual information from synchronized video as auxiliary cues to indicate the type of noise, which eventually improves the speech enhancement performance. Specifically, we propose the VC-S$^2$E method, which incorporates the Conformer and Mamba modules for their complementary strengths. Extensive experiments are conducted on public MUSIC, AVSpeech and AudioSet datasets, where the results demonstrate the superiority of VC-S$^2$E over other competitive methods. We will make the source code publicly available. Project demo page: https://AVSEPage.github.io/
Social Networks and Human-Centered Computing Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所台湾国际研究生计划社交网络与人本计算项目）；Department of Computer Science, National Chengchi University（国立政治大学资讯科学系）；Institute of Information Systems and Applications, National Tsing Hua University（国立清华大学资讯系统与应用研究所）；National Institute of Informatics（国立情报学研究所）；Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University（国立清华大学电机工程学系与通讯工程研究所）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）
AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification
College of Automation Engineering, Nanhang University（南京航空航天大学自动化工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Protecting Bystander Privacy via Selective Hearing in Audio LLMs
VRAIN, Universitat Politècnica de València（VRAIN，瓦伦西亚理工大学）；Department of Engineering, University of Cambridge（剑桥大学工程系）；INGENIO (CSIC-Universitat Politècnica de València)（INGENIO（CSIC-瓦伦西亚理工大学））
Selective Listening: Mechanism-Guided Control of Audio Influence in Large Audio-Language Models
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）
I Can Hear You: Selective Robust Training for Deepfake Audio Detection
Columbia University（哥伦比亚大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；New York University（纽约大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Rutgers University（罗格斯大学）
Audio-Visual Target Speaker Extraction with Reverse Selective Auditory Attention
National University of Singapore（新加坡国立大学）；University of Science and Technology Beijing（北京科技大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big data（深圳大数据研究院）；University of Bremen（不来梅大学）
Predicting Upcoming Stuttering Events from Three-Second Audio: Stratified Evaluation Reveals Severity-Selective Precursors, and the Model Deploys Fully On-Device
Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrieval
Zhejiang University（浙江大学）；Peking University（北京大学）；Zhejiang Gongshang University（浙江工商大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）
Cross-attention Inspired Selective State Space Models for Target Sound Extraction
National Key Laboratory of General Artificial Intelligence（国家级通用人工智能重点实验室）；School of Intelligence Science and Technology（智能科学与技术学院）；Peking University（北京大学）
An overview of neural architectures for self-supervised audio representation learning from masked spectrograms
Department of Electronic Systems, Aalborg University（电子系统系，奥尔堡大学）；Pioneer Centre for Artificial Intelligence（人工智能先锋中心）；National and Kapodistrian University of Athens（雅典国家与卡波蒂斯坦大学）
Enhancing Crowdsourced Audio for Text-to-Speech Models
Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）；Centre de Llenguatge i Computació (CLiC), Universitat de Barcelona（巴塞罗那大学语言与计算中心）；Universität Zürich（苏黎世大学）
本文针对众包音频质量不足的问题，在 Common Voice 加泰罗尼亚语子集上提出音频增强加 NISQA 自动过滤的去噪流水线，使扩散 TTS 模型的 UTMOS 分数提升 0.4。
DeCodec: Rethinking Audio Codecs as Universal Disentangled Representation Learners
Jiutian Artificial Intelligence Research Institute, China Mobile, China State Key Laboratory of Multimedia Information Processing, Peking University, China（九天人工智能研究院，中国移动，国家多媒体信息处理重点实验室，北京大学）
