PhD thesis: A Machine Learning Approach to Automatic Chord Extraction

BristolTo celebrate the 1-year anniversary of defending my Thesis, I thought it was time to make it public! Writing and defending this weighty tome took the best part of 4 years and were simultaneously some of the most enjoyable and stressful years of my life so far.

A full list of thanks and acknowledgements can be found in the text itself, but let me state that this would not have been possible without the support of my family, my advisor Tijl De Bie, the Bristol Centre for Complexity Sciences, and the Engineering and Physical Sciences Research Council. The salient details of the thesis are:

  • Author: Matt McVicar, University of Bristol
  • Title: A Machine Learning Approach to Automatic Chord Extraction
  • Supervisor: Dr Tijl De Bie, Intelligent Systems Laboratory, University of Bristol
  • Internal examiner: Dr Peter Flach, Intelligent Systems Laboratory, University of Bristol
  • External examiner: Dr Simon Dixon, Centre for Digital Music, Queen Mary University of London
  • Defence date: February 12th 2013

The main contributions of this work, and the associated publications, are:

  1. A thorough review of automatic chord extraction, in particular machine learning and expert systems [ 1 ]
  2. A new machine learning-based model for the automatic extraction of chords, chord inversions and musical keys [ 2 ]
  3. The application of this model to unseen data, in particular the use of large numbers of freely-available chord sequences from the internet [ 3, 4 ]

[ 1 ] M. McVicar, R. Santos-Rodríguez, Y. Ni and T. De Bie. Automatic Chord Estimation: A Review of the State of the Art. IEEE Transactions on Audio, Speech and Language Processing, Overview Article

[ 2 ] Y. Ni, M. McVicar, R. Santos-Rodríguez. and T. De Bie. An end-to-end machine learning system for harmonic analysis of music. IEEE Transactions on Audio, Speech and Language Processing

[ 3 ] M. McVicar, Y. Ni, R. Santos-Rodríguez. and T. De Bie. Using Online Chord Databases to Enhance Chord Recognition. Journal of New Music Research, Special Issue on Music and Machine Learning

[ 4 ] M. McVicar, Y. Ni, R. Santos-Rodríguez and T. De Bie. Curriculum Learning on Large Chord Databases. In Proceedings of the 12th International Society for Music Information Retreival (ISMIR), 2011

The full text (after corrections) and publications can be accessed at my Publications page.

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