Musical keys and chords recognition using unsupervised learning with infinite Gaussian mixture

• Authors: Yun-Sheng Wang;Harry Wechsler
• Venue: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
• Year: 2012

This paper presents a Bayesian-based methodology that determines keys and chords of symbolic music using unsupervised learning guided by constraints. An infinite Gaussian mixture, a type of Dirichlet Process mixture, is constructed to model the generative processes of keys and chords. Keys and chords are recognized iteratively by generated key and chord samples using the same algorithm. The model only uses very simple profiles for keys and chords to guide the learning process without the use of training data or rules. The technique is also capable of recognizing key modulations. We demonstrate the performance of the proposed approach by comparing it against existing methods using 159 songs from the Beatles MIDI collection. The results show that the proposed method performs well in both keys and chords finding.