Paper Archive

We introduce FoleySet, a human-annotated Foley sound dataset designed to support research on sound-event understanding and Foley sound generation. The dataset provides annotations at multiple levels of granularity, capturing both broad event categories and more detailed semantic or production-related attributes. This multi-level structure supports tasks such as classification, retrieval, captioning, and controllable generation. FoleySet is intended to address the limited availability of systematically annotated Foley material and to provide a common resource for evaluating models across different levels of semantic detail.

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Sound effects (SFX) datasets and libraries often employ distinct tagging schemes, taxonomies, and metadata structures. This creates challenges for research on SFX classification and generation because incompatible taxonomies lead to siloed datasets that might require individualized approaches, result in non-comparable outcomes, and prevent data merging strategies. We propose a modular dataset relabeling framework that adopts the Universal Category System (UCS), an industry-standard hierarchical taxonomy for sound effects, as a shared structural foundation. This open-source framework enables us (i) to convert tags of existing datasets to UCS with a rule-based multi-stage pipeline and conflict resolution to achieve high automatic conversion rates, (ii) to suggest a stratified dataset split for the new labels, and (iii) to combine multiple datasets. To showcase the practical utility, we introduce the EnvSound-UCS dataset, a publicly available unified UCS-compliant dataset of environmental sounds with 58,057 sound clips from three sources: AudioSet, FSD50K, and ESC-50.

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