Publications

Peer-Reviewed Journal Papers

  1. M. McVicar, B. Sach C. Mesnage, J. Lijffijt, E. Spyropoulou, T. De Bie. SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees. Pattern Recognition Letters, 2016 [pdf][bibtex]
  2. M. McVicar, S. Fukayama, M. Goto. AutoGuitarTab: Computer-aided Composition of Rhythm & Lead Guitar Parts in the Tablature Space. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2015. [pdf][bibtex]
  3. 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. [pdf][bibtex]
  4. Y. Ni, M.McVicar, Santos-Rodríguez and T. De Bie. Understanding Effects of Subjectivity in measuring Chord Estimation Accuracy. IEEE Transactions on Audio, Speech and Language Processing. [pdf][bibtex]
  5. 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.  [pdf][bibtex]
  6. 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. [pdf][bibtex]

Peer-Reviewed Conference and Workshop Papers

  1. M. McVicar, R. Santos-Rodríguez and T. De Bie. Learning to Separate Vocals from Polyphonic Mixtures via Ensemble Methods and Structured Output Prediction. In Proceedings of the IEEE International Conference on Audio, Speech, and Language Processing (ICASSP), 2016. [pdf][bibtex]
  2. C. Mesnage, R. Santos-Rodríguez, M. McVicar, T. De Bie. Trend Extraction on Twitter Time Series for Music Discovery. Workshop on Machine Learning for Music Discovery at the International Conference on Machine Learning, 2015. [pdf][bibtex]
  3. M. McVicar, C. Mesnage, J. Lijffijt, T. De Bie. Interactively Exploring Supply and Demand in the UK Independent Music Scene. Demo track, 14th joint European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, 2014. [pdf][bibtex]
  4. M. McVicar, C. Mesnage, J. Lijffijt, E. Spyropoulou, T. De Bie. Supply and demand of independent UK music artists on the web. Proceedings of the Web Science conference, 2015. [pdf][bibtex]
  5. B. McFee, C. Raffel, D. Liang, D. Ellis, M. McVicar, E. Battenberg, O. Nieto. librosa: Audio and Music Signal Analysis in Python. In proceedings of the 14th Python in Science conference, 2015. [pdf][bibtex]
  6. M. McVicar, S. Fukayama, M. Goto. AutoLeadGuitar: Automatic generation of guitar solo phrases in the tablature space. In proceedings of the IEEE sponsored International Conference on Signal Processing, 2014. [pdf][bibtex]
  7. M. McVicar, S. Fukayama, M.Goto. AutoRhythmGuitar: Computer-aided Composition for Rhythm Guitar in the Tab Space. In Proceedings of the International Computer Music Conference and Sound and Music Computing Conference (ICMCSMC), 2014. [pdf][bibtex]
  8. M.McVicar, D.Ellis, M.Goto. Leveraging repetition for improved automatic lyric transcription in popular music. In Proceedings of the IEEE International Conference on Audio, Speech, and Language Processing (ICASSP), 2014. [pdf][bibtex] 
  9. Y. Ni, M. McVicar, R. Santos-Rodríguez. and T. De Bie. Using Hyper genre-training to explore genre information for automatic chord estimation. In Proceedings of the 13th International Society for Music Information Retreival (ISMIR), 2012. [pdf][bibtex]
  10. M. McVicar and T. De Bie. CCA and a Multi-way Extension for Investigating
    Common Components between Audio, Lyrics and Tags. In proceedings of 9th International Symposium on Computer Music Modeling and Retrieval (CMMR), 2012. [pdf] [bibtex]
  11. M. McVicar, Y. Ni, R. Santos-Rodríguez and T. De Bie. Leveraging Noisy Online Databases for Use in Chord Recognition. In Proceedings of the 12th International Society for Music Information Retreival (ISMIR), 2011.[pdf] [bibtex]
  12. M. McVicar, T. Freeman and T. De Bie. Mining the Correlation Between Lyrical and Audio Features and the Emergence of Mood. In Proceedings of the 12th International Society for Music Information Retreival (ISMIR), 2011. [pdf] [bibtex]
  13. Y.Ni, SantosRodríguez, MattMcVicar and T.DeBie. Hit Song Science Once Again a Science? In 4th International Workshop on Machine Learning and Music, 2011. [pdf] [bibtex]
  14. M. McVicar and T. De Bie. Enhancing chord recognition accuracy using web resources. In 3rd International Workshop on Machine Learning and Music, 2010. [pdf] [bibtex]

Other Publications

  1. Y.Ni,M.Mcvicar,R.Santos-Rodriguez and T.DeBie. Harmony Progression Analyzer for MIREX 2011. [pdf] [bibtex]
  2. Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez and Tijl De Bie. Meta-Song evaluation for Chord Recognition. 12th International Society for Music Information Retreival (ISMIR), 2011, Late-breaking extended abstract. [pdf][bibtex]
  3. Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez and Tijl De Bie. Meta-song evaluation for chord recognition. [pdf][bibtex]
  4. M. McVicar and T. De Bie. Exploiting Online Resources to Improve Chord Recognition Accuracy. 11th International Society for Music Information Retrieval (ISMIR), 2010, Late-breaking extended abstract. [pdf] [bibtex]

PhD Thesis

A Machine Learning Approach to Automatic Chord Extraction [pdf] [bibtex]

 

 

 

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