Abstract. The identification of recurrent patterns in chord progressions into meaningful units is a useful tool for a variety of tasks in automatic music processing and analysis, e.g. for the identification of song sections and the induction of song structure, sytle identiication and analysis, cover song detection, and the modelling of harmonic expectations. We propose an unsupervised approach that identifies cadences (i.e. recurrent patterns that indicate harmonic closure) from entropy profiles of chord progressions from symbolic harmony annotations for pop songs. Results indicate a large overlap between the most highly ranked progressions of 3 subsequent chords and well-known cadences from music theory. Further research will go into segmentation of chords into meaningful episodes, n-gram models and semantic clustering.
@inproceedings{mauch:fcb:2007,
Author = {Mauch, Matthias and M\"ullensiefen, Daniel and Wiggins, Geraint and Dixon, Simon},
Booktitle = {Digital Music Research Network meeting (DMRN 2007)},
Title = {Finding Cadences through Boundary Entropy},
Year = {2007}}