MediaEval 2014: A Multimodal Approach to Drop Detection in Electronic Dance Music

Abstract

We predict drops in electronic dance music (EDM), employing different multimodal approaches. We combine three sources of data: noisy labels collected through crowdsourcing, timed comments from SoundCloud and audio content analysis. We predict the correct labels from the noisy labels using the majority vote and Dawid-Skene methods. We also employ timed comments from SoundCloud users to count the occurrence of specific terms near the potential drop event, and, finally, we conduct an acoustic analysis of the audio excerpts. The best results are obtained, when both annotations, metadata and audio, are combined, though the differences between them are not significant.