Automatically inferring the structural properties of raw multimedia documents is essential in today’s digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.
J. Serrà, M. Müller, P. Grosche, and J. Ll. Arcos. Unsupervised music structure annotation by time series structure features and segment similarity. IEEE Trans. on Multimedia, Special Issue on Music Data Mining. In press.
Note that this is the most complete report of the system that achieved very good results at the MIREX 2012 Structural Segmentation task and that we provide the binaries for the system here.