Assigning a Confidence Threshold on Automatic Beat Annotation in Large Datasets

TitleAssigning a Confidence Threshold on Automatic Beat Annotation in Large Datasets
Publication TypeConference Paper
Year of Publication2012
Conference Name13th International Society for Music Information Retrieval Conference
AuthorsZapata, J. R., Holzapfel A., Davies M. E. P., Oliveira J., & Gouyon F.
Conference Start Date08/10/2012
Conference LocationPorto, Portugal
Keywordsbeat tracking, database
AbstractIn this paper we establish a threshold for perceptually acceptable beat tracking based on the mutual agreement of a committee of beat trackers. In the first step we use an existing annotated dataset to show that mutual agreement can be used to select one committee member as the most reliable beat tracker for a song. Then we conduct a listening test using a subset of the Million Song Dataset to establish a threshold which results in acceptable quality of the chosen beat output. For both datasets, we obtain a percent age of trackable music of about 73%, and we investigate which data tags are related to acceptable and problematic beat tracking. The results indicate that current datasets are biased towards genres which tend to be easy for beat tracking. The proposed methods provide a means to automatically obtain a confidence value for beat tracking in non-annotated data and to choose between a number of beat tracker outputs.
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