BAF: an audio fingerprinting dataset for broadcast monitoring

Cortès, Guillem; Ciurana, Alex; Molina, Emilio; Miron, Marius; Meyers, Owen; Six, Joren; Serra, Xavier

doi:http://dx.doi.org/10.5281/zenodo.7372162

BAF: an audio fingerprinting dataset for broadcast monitoring

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Citation

Cortès G, Ciurana A, Molina E, Miron M, Meyers O, Six J, Serra X. BAF: an audio fingerprinting dataset for broadcast monitoring. In: Rao P, Murthy H, Srinivasamurthy A, Bittner R, Caro Repetto R, Goto M, Serra X, Miron M, editors. Proceedings of the 23nd International Society for Music Information Retrieval Conference (ISMIR 2022); 2022 Dec 4-8; Bengaluru, India. [Canada]: International Society for Music Information Retrieval; 2022. p. 908-16. DOI: 10.5281/zenodo.7372162

Abstract

Audio Fingerprinting (AFP) is a well-studied problem in music information retrieval for various use-cases e.g. content-based copy detection, DJ-set monitoring, and music excerpt identification. However, AFP for continuous broadcast monitoring (e.g. for TV & Radio), where music is often in the background, has not received much attention despite its importance to the music industry. In this paper (1) we present BAF, the first public dataset for music monitoring in broadcast. It contains 74 hours of production music from Epidemic Sound and 57 hours of TV audio recordings. Furthermore, BAF provides cross-annotations with exact matching timestamps between Epidemic tracks and TV recordings. Approximately, 80% of the total annotated time is background music. (2) We benchmark BAF with public state-of-the-art AFP systems, together with our proposed baseline PeakFP: a simple, non-scalable AFP algorithm based on spectral peak matching. In this benchmark, none of the algorithms obtain a F1-score above 47%, pointing out that further research is needed to reach the AFP performance levels in other studied use cases. The dataset, baseline, and benchmark framework are open and available for research.