🎵 Audio & Speech

🎞️ ECCV2026 · 5 paper notes

📌 Same area in other venues: 📷 CVPR2026 (22) · 🔬 ICLR2026 (80) · 💬 ACL2026 (71) · 🧪 ICML2026 (36) · 🤖 AAAI2026 (30) · 🧠 NeurIPS2025 (47)

🔥 Top topics: Speech & Audio ×2

LipsFlow: First Exploration of Neuromorphic OT-CFM in Multi-Speaker Visual Speech Recognition

LipsFlow for the first time introduces neuromorphic event camera perception and optimal transport conditional flow matching (OT-CFM) to multi-speaker visual speech recognition. By capturing millisecond-level lip dynamics through learnable event representations, implementing deterministic 2-step ODE decoding with OT-CFM, and resolving homophenous ambiguities via dual-level semantic supervision, it achieves state-of-the-art performance on DVS-Lip with a 22.3% WER and 240ms latency.

MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization

MG-RWKV proposes a temporal forgery localization (TFL) framework based on the linear-complexity recurrent architecture of RWKV. By capturing global temporal context via bidirectional RWKV, adaptively selecting explicit temporal receptive fields through a Multi-Grained Mixture of Experts (MG-MoE), and eliminating contradictory predictions across multi-scale features using a Cross-Grained Consistency constraint (CGC), it comprehensively outperforms previous state-of-the-art methods on four benchmarks: Lav-DF, TVIL, Psynd, and AV-Deepfake1M, while maintaining \(O(T)\) linear complexity with an inference time of only 73.4ms.

See & Sniff: Learning Visuo-Olfactory Representations

Using the key insight that "odor identity remains invariant to visual transformations within semantic categories," this paper extends the smell-only SmellNet dataset into a paired visuo-olfactory dataset, SmellNet-V. It designs a self-supervised dual-stream framework named See & Sniff based on dense local contrastive alignment. The proposed method significantly outperforms smell-only baselines in odor classification, cross-modal retrieval, and a newly introduced pixel-level odor localization task.

Sparsity-Inducing Divergence Losses for Biometric Verification

Q-Margin shifts the margin penalty from geometric logit modifications to a probabilistic reference measure in \(\alpha\)-divergence losses. While maintaining posterior sparsity, it consistently outperforms ArcFace/CosFace baselines in low-FAR scenarios on IJB-B/C and VoxCeleb, and reduces training throughput overhead from 27% to 5% via exact top-K truncation.

Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance

Addressing the limitation where existing video-to-audio (V2A) models can only generate a complete audio track all at once instead of adding sounds layer-by-layer like Foley artists, this paper proposes Negative Audio Guidance (NAG). By training a branch conditioned on "already-generated audio tracks" and utilizing it inversely during sampling, the current generation is pushed away from existing sounds. This enables step-by-step generation of complementary audio tracks to mix into high-quality synthesized sound, using only standard single-reference audio-visual datasets.