Automatic performer identification in commercial monophonic Jazz performances

We present a pattern recognition approach to the task of identifying performers from their interpretative styles. We investigate how professional musicians express their view of the musical content of musical pieces and how to use this information in order to automatically identify performers. We apply sound analysis techniques based on spectral models for extracting deviation patterns of parameters such as pitch, timing, amplitude and timbre characterising both the internal structure of notes and the musical context in which they appear. We describe successful performer identification case studies involving monophonic audio recordings of both score-guided and commercial improvised performances.