# The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure

## TL;DR

This study analyzes how users of music streaming services tend to skip songs at points that correlate with musical structure, revealing insights into user behavior and enabling improved prediction of musical structure from acoustic data.

## Contribution

It establishes a correlation between skipping behavior and musical structure, and demonstrates that large-scale behavioral data can enhance musical structure prediction models.

## Key findings

- Skips are aligned with musical structural points.
- Distribution of skips is consistent across users and time.
- Behavioral data improves accuracy of musical structure prediction.

## Abstract

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.