New methods in structural segmentation of musical audio

We describe a simple model of musical structure and two related methods of extracting a high-level segmentation of a music track from the audio data, including a novel use of hidden semi-Markov models. We introduce a semi-supervised segmentation process which finds musical structure with improved accuracy given some very limited manual input. We give experimental results compared to existing methods and human segmentations.