FSD50K: An Open Dataset of Human-Labeled Sound Events

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and encompassing over 500 sound classes. However, AudioSet is not an open dataset as its official release consists of pre-computed audio features. Downloading the original audio tracks can be problematic due to YouTube videos gradually disappearing and usage rights issues. To provide an alternative benchmark dataset and thus foster SER research, we introduce FSD50K, an open dataset containing over 51 k audio clips totalling over 100 h of audio manually labeled using 200 classes drawn from the AudioSet Ontology. The audio clips are licensed under Creative Commons licenses, making the dataset freely distributable (including waveforms). We provide a detailed description of the FSD50K creation process, tailored to the particularities of Freesound data, including challenges encountered and solutions adopted. We include a comprehensive dataset characterization along with discussion of limitations and key factors to allow its audio-informed usage. Finally, we conduct sound event classification experiments to provide baseline systems as well as insight on the main factors to consider when splitting Freesound audio data for SER. Our goal is to develop a dataset to be widely adopted by the community as a new open benchmark for SER research.

Automated summary

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific. AudioSet, which is based on over 2 M tracks from YouTube videos, is not an open dataset. To address this issue, researchers introduce FSD50K, an open dataset containing over 51k audio clips manually labeled using 200 classes from the AudioSet Ontology. The dataset is freely distributable and detailed descriptions and analysis are provided. Baseline systems and insights on data splitting for SER are also presented.