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Measuring ensemble interdependence in a string quartet through analysis of multidimensional performance data. Frontiers in Psychology. 5,(2014).
The Sense of Ensemble: a Machine Learning Approach to Expressive Performance Modelling in String Quartets. Journal of New Music Research. 43, 303-317.(2014).
Multidimensional analysis of interdependence in a string quartet. International Symposium on Performance Science.(2013).
Aural-based detection and assessment of real versus artiﬁcially synchronized string quartet performance. 3rd International Conference on Music & Emotion.(2013).
Inducing rules of ensemble music performance: a machine learning approach. 3rd international conference on Music & Emotion, Jyväskylä.(2013).
Investigating the relationship between expressivity and synchronization in ensemble performance: an exploratory study. International Symposium on Performance Science, Vienna.(2013).
repoVizz: a Framework for Remote Storage, Browsing, Annotation, and Exchange of Multi-modal Data. ACM International Conference on Multimedia (MM'13).(2013).
Computational analysis of solo versus ensemble performance in string quartets: Dynamics and Intonation. 12th International Conference on Music Perception and Cognition.(2012).
A hair ribbon deflection model for low-intrusiveness measurement of bow force in violin performance.. New Interfaces for Musical Creation (NIME 2011).(2011).
Measuring ensemble synchrony through violin performance parameters: a preliminary progress report. 2nd Workshop of Social Behavior in Music (SBM-2011/INTETAIN-2011).(2011).
Synchronization of intonation adjustments in violin duets: towards an objective evaluation of musical interaction. Proc. of the 14th Int. Conference on Digital Audio Effects (DAFx-11).(2011).
A Lyrics-Matching QBH System for Interactive Environments. Sound and Music Computing Conference.(2010).
Kettle: A Real-time model for Orchestral Timpani. Sound and Music Computing.(2010).