Identification of live or studio versions of a song via supervised learning

We aim to distinguish between the “live” and “studio” versions of songs by using supervised techniques. We show which segments of a song are the most relevant to this classification task, and we also discuss the relative importance of audio, music and acoustic features, given this challenge. This distinction is crucial in practice since the listening experience of the user of online streaming services is often affected, depending on whether the song played is the original studio version or a secondary live recording. However, manual labelling can be tedious and challenging. Therefore, we propose to classify automatically a music data set by using Machine Learning techniques under a supervised setting. To the best of our knowledge, this issue has never been addressed before. Our proposed system is proven to perform with high accuracy on a 1066-song data set with distinct genres and across different languages.