Wedescribeaprobabilisticmodelforlearningmusicalkeyprofiles from symbolic files of polyphonic, classical music. Our model is based on Latent Dirichlet Allocation (LDA), a statistical approach for discovering hidden topics in large corpora of text. In our adaptation of LDA, symbolic music files play the role of text documents, groups of musical notes play the role of words, and musical keyprofiles play the role of topics. The topics are discovered as significant, recurring distributions over twelve neutral pitch-classes. Though discovered automatically, these distributions closely resemble the traditional key-profiles used to indicate the stability and importance of neutral pitchclasses in the major and minor keys of western music. Unlike earlier approaches based on human judgement, our model learns key-profiles in an unsupervised manner, inferringthemautomaticallyfromalargemusicalcorpusthat contains no key annotations. We show how these learned key-profiles can be used to determine the key of a musical piece and track its harmonic modulations. We also show how the model’s inferences can be used to compare musical pieces based on their harmonic structure.
['Diane J. Hu', 'Lawrence K. Saul']
A PROBABILISTIC TOPIC MODEL FOR UNSUPERVISED LEARNING OF MUSICAL KEY-PROFILES