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My research interests lie in the area of machine learning and analysis of time-based data, music representation, and e-learning applications. I am particularly interested in Deep Learning methods for combining and integrating signals with symbolic information and logic.

I was the Principal Investigator of

  • AHRC project An Integrated Audio-Symbolic Model of Music Similarity
  • AHRC project Digital Music Lab Project.
  • I was the Co-Investigator in

  • EPSRC/Innovate UK Advancing Consumer Protection Through Machine Learning: Reducing Harm in Gambling
  • AHRC project Digital Music Lab Project.
  • I was a member of the MPEG Ad Hoc Group on Symbolic Music Representation, where we developed the SMR standard for music notation and structural information within MPEG-4.

    I was coordinator of the MUSITECH project which provides a platform for musical applications.

    I was a consultant to the NEUMES project at Harvard University.

    In my doctoral dissertation I developed the Integrated Segmentation and Similarity Model for music analysis.

    I am co-author of the music education software "Computer Courses in Music - Ear Training" which has been publishd by Schott.

    In my mster's thesis I developped a grammer for chord sequences in jazz (see Publications for a paper on it).