Download FoleySet: A Multi-Level Human-Annotated Foley Sound Dataset 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.
Download A hierarchical approach to automatic musical genre classification A system for the automatic classification of audio signals according to audio category is presented. The signals are recognized as speech, background noise and one of 13 musical genres. A large number of audio features are evaluated for their suitability in such a classification task, including well-known physical and perceptual features, audio descriptors defined in the MPEG-7 standard, as well as new features proposed in this work. These are selected with regard to their ability to distinguish between a given set of audio types and to their robustness to noise and bandwidth changes. In contrast to previous systems, the feature selection and the classification process itself are carried out in a hierarchical way. This is motivated by the numerous advantages of such a tree-like structure, which include easy expansion capabilities, flexibility in the design of genre-dependent features and the ability to reduce the probability of costly errors. The resulting application is evaluated with respect to classification accuracy and computational costs.