67%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026TsinghuaTsinghua University, TsinghuaQinghaiSpeech enhancementEfficient Streaming Audio-Visual Target Speaker Extraction for Real-World Acoustic ScenesWendi Sang, Kai Li, Yifan Li, Jianqiang Huang and 1 moreSydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026– ReadersNo votes yet2/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 2 of 20 reviewers recommend itlenient 2/5medium 0/10strict 0/5
67%Highly rated?Highly ratedVote to see the scoreNeurIPS 2026The Chinese University of Hong KHuawei Technologies Ltd.Chinese University of Hong Kong,Speech enhancementAnyEdit: A Unified Framework for Speech and Singing Voice Editing with Real-World Environmental ConsistencyYunjia Zhang, Junan Zhang, Jing Yang, Xueyao Zhang and 2 moreSydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026– ReadersNo votes yet2/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 2 of 20 reviewers recommend itlenient 2/5medium 0/10strict 0/5
45%Niche pick?Niche pickVote to see the scoreNeurIPS 2026Ben Gurion University of the NegTel Aviv University and FacebookSpeech enhancementAudioGS: High-Fidelity Neural Audio Compression via Continuous Gaussian SplattingRon Aluf, Alon Canfi, Eliya NachmaniParis Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026– ReadersNo votes yet0/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 0 of 20 reviewers recommend itlenient 0/5medium 0/10strict 0/5
88%Must read?Must readVote to see the scoreNeurIPS 2026NVIDIANational TaiwanYuan ZeAltai StateAcademia SinicaSpeech enhancementRethinking Training Targets, Architectures and Data Quality for Universal Speech EnhancementTime-shifted anechoic targets, a two-stage distortion-perception framework, and curated data improve universal speech enhancement and achieve state-of-the-art results.Szu-Wei Fu, Rong Chao, Xuesong Yang, Sung-Feng Huang and 5 moreSydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 8 on Hugging Face– ReadersNo votes yet15/20 AI panelreviewers recommend itReaders and the AI panel: vote on this paper to see what they said.Worth readingNot for meOnly vote on papers you've read. Sign in with GitHub to vote.AI panel: 15 of 20 reviewers recommend itlenient 5/5medium 9/10strict 1/5