Detecting Socially Significant Music Events using Temporally Noisy Labels

In this paper, we focus on event detection over the timeline of a music track. Such technology is motivated by the need for innovative applications such as searching, non-linearaccess and recommendation. Event detection over the timeline requires time-code level labels in order to train machine learning dels. We use timed comments from SoundCloud, a modern social music sharing platform, to obtain these labels. While in this way the need for tedious and time-consuming manual labeling can be reduced, the challenge is that timed comments are subject to additive temporal noise, as they are in the temporal neighborhood of the actual events. We investigate the utility of such noisy timed comments as training labels through a case study, in which we investigate three types of events in Electronic Dance Music (EDM): drop, build and break. These socially significant events play a key role in an EDM track's unfolding and are popular in social media circles. They are therefore not only interesting for detection, but also typically accompanied by timed comments resulting from the online social activity around them. We propose a two-stage learning method that relies on noisy timed comments and, given a music track, marks the events on the timeline. In the experiments, we focus in particular on investigating to which extent noisy timed comments can replace manually added expert labels. The conclusions we draw during this study provide useful insights that motivates further research in the field of event detection.