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A Framework for Performer Identification in Audio Recordings

A Framework for Performer Identification in Audio Recordings

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled 2009. We present a general framework for the task of identifying performers from their playing styles. We investigate how musicians express and communicate their view of the musical content in pieces and how to use this information in order to automatically identify performers. We study note-level deviations of parameters such as timing and amplitude. Our approach to performer identification consists of inducing an expressive performance model for each of the interpreters (essentially establishing a performer dependent mapping of inter-note features to a timing and amplitude expressive transformations). We outline two successful performer identification case studies.


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