Violinist identification based on vibrato features
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Identifying performers from polyphonic music is a challenging task in music information retrieval. As a ubiquitous expressive element in violin music, vibrato contains important information about the performers' interpretation. This paper proposes to use vibrato features for identifying violinists from commercial orchestral recordings. We present and compare two systems, which take the same note-level melodies as input while using different vibrato feature extractors and classification schemes. One system calculates vibrato features according to vibrato definition, models the feature distribution using histograms, and classifies performers based on the distribution similarity. The other system uses the adaptive wavelet scattering which contains vibrato information and identifies violinists with a machine learning classifier. We report accuracy improvement of 19.8% and 17.8%, respectively, over a random baseline on piece-level evaluation. This suggests that vibrato notes in polyphonic music are useful for master violinist identification.