VP-SelDoA: Visual-prompted Selective DoA Estimation of Target Sound via Semantic-Spatial Matching

Yu Chen, Xinyuan Qian, Hongxu Zhu, Jiadong Wang, Kainan Chen, Haizhou Li

Abstract: Audio-visual sound source localization (AV-SSL) estimates the position of sound sources by fusing auditory and visual cues. Current AV-SSL methodologies typically require spatially-paired audio-visual data and cannot selectively localize specific target sources. To address these limitations, we introduce Cross-Instance Audio-Visual Localization (CI-AVL), a novel task that localizes target sound sources using visual prompts from different instances of the same semantic class. CI-AVL enables selective localization without spatially paired data. To solve this task, we propose AV-SSAN, a semantic-spatial alignment framework centered on a Multi-Band Semantic-Spatial Alignment Network (MB-SSA Net). MB-SSA Net decomposes the audio spectrogram into multiple frequency bands, aligns each band with semantic visual prompts, and refines spatial cues to estimate the direction-of-arrival (DoA). To facilitate this research, we construct VGGSound-SSL, a large-scale dataset comprising 13,981 spatial audio clips across 296 categories, each paired with visual prompts. AV-SSAN achieves a mean absolute error of 16.59 and an accuracy of 71.29%, significantly outperforming existing AV-SSL methods. Code and data will be public.

External IDs:dblp:journals/corr/abs-2507-07384