Abstract:

This paper presents a new approach for identifying professional
performers in commercial recordings.
We propose a Trend-based model that, analyzing the way Narmour's
Implication-Realization patterns are played, is able to characterize
performers.
Concretely, starting from automatically extracted descriptors provided
by state-of-the-art extraction tools, the system performs a mapping to
a set of qualitative behavior shapes and constructs a collection of
frequency distributions for each descriptor.
Experiments were conducted in a data-set of violin recordings from 23
different performers.
Reported results show that our approach is able to achieve high
identification rates.

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