The behavior of users of music streaming services is investigated from the point of view of the temporal dimension of individual songs; specifically, the main object of the analysis is the point in time within a song at which users stop listening and start streaming another song ("skip"). The main contribution of this study is the ascertainment of a correlation between the distribution in time of skipping events and the musical structure of songs. It is also shown that such distribution is not only specific to the individual songs, but also independent of the cohort of users and, under stationary conditions, date of observation. Finally, user behavioral data is used to train a predictor of the musical structure of a song solely from its acoustic content; it is shown that the use of such data, available in large quantities to music streaming services, yields significant improvements in accuracy over the customary fashion of training this class of algorithms, in which only smaller amounts of hand-labeled data are available.
Research on the topic has long been hindered by the lack of sizeable amounts of expert annotations -- the manual segmentations and labelings of recordings done by musically-competent individuals -- because of its time-consuming nature, coupled with the legal retrictions involved in to sharing copyrighted recordings. Initiatives such as the SALAMI dataset @cite_13 attempt to fill this need by providing a relatively large (several hundreds) source of annotations for recordings, many of which are in the public domain.
@cite_13
2293125622
This paper describes the design and creation of an unprecedentedly large database of over 2400 structural annotations of nearly 1400 musical recordings. The database is intended to be a test set for algorithms that will be used to analyze a much larger corpus of hundreds of thousands of recordings, as part of the Structural Analysis of Large Amounts of Musical Information (SALAMI) project. This paper describes the design goals of the database and the practical issues that were encountered during its creation. In particular, we discuss the selection of the recordings, the development of an annotation format and procedure that adapts work by Peeters and Deruty [10], and the management and execution of the project. We also summarize some of the properties of the resulting corpus of annotations, including average inter-annotator agreement.