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 ...
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.
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