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C4DM Seminar: Eita Nakamura - Recent Developments in Statistical Modelling Techniques for Symbolic Music Processing

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Date and Time
Wednesday, 10th May 2017, at 4:00pm

GC222, Graduate Centre, Queen Mary University of London, Mile End Road, London E1 4NS. Information on how to access the school can be found at here.

Eita Nakamura

Recent Developments in Statistical Modelling Techniques for Symbolic Music Processing

Statistical models used in natural language processing have been successfully applied to music processing, but characteristic structures found in music data, e.g. the polyphonic structure and the repetitive structure, call for new models and learning techniques. I review recently developed modelling techniques for capturing these structures (based on publications found at In the modelling dimension, merged-output HMMs for describing the polyphonic structure and unsupervised Bayesian learning techniques for capturing the repetitive structure are explained. In the application dimension, symbolic music alignment, automatic music accompaniment, automatic music arrangement, symbolic music transcription, automatic decision of piano fingering, etc. are discussed with demonstrating examples and introductions to ongoing work.

Eita Nakamura is a JSPS Postdoctoral Research Fellow in the Speech and Audio Processing Group at Kyoto University and he is currently a visitor at the Centre for Digital Music at Queen Mary University of London. He received a PhD in Physics at the University of Tokyo in 2012 and have published papers on various topics on symbolic music processing including automatic music accompaniment, music transcription, and automatic music arrangement. His research interests include music modelling and analysis, music information processing, statistical machine learning, and application of complex systems for music phenomena.

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