A genetic rule-based model of expressive performance for jazz saxophone
Computer Music Journal
Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expressive concatenative synthesis by reusing samples from real performance recordings
Computer Music Journal
Identifying violin performers by their expressive trends
Intelligent Data Analysis - Machine Learning and Music
A state of the art on computational music performance
Expert Systems with Applications: An International Journal
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We propose a novel approach to the task of identifying performers from their playing styles. We investigate how skilled musicians (Jazz saxophone players in particular) express and communicate their view of the musical and emotional content of musical pieces and how to use this information in order to automatically identify performers. We study deviations of parameters such as pitch, timing, amplitude and timbre both at an inter-note level and at an intra-note level. Our approach to performer identification consists of establishing a performer dependent mapping of inter-note features (essentially a "score" whether or not the score physically exists) to a repertoire of inflections characterized by intra-note features. We present a successful performer identification case study