Year: 2025
Type: conference-paper
Abstract: Identifying individual instruments of the same type from their recordings is a rarely addressed challenge.This paper explores violin sound identification using two datasets of recordings, featuring multiple violinists playing multiple violins.Several long-term audio features were compared, and their performance in violin classification was evaluated using classical machine learning algorithms.Among these features, long-term MFCCs demonstrated the ability to distinguish individual violins while taking into account player-induced variability, enabling reliable violin recognition.Additionally, the influence of key parameters -such as the number of violinists and choice of musical excerpts -on classification performance was analyzed.These findings offer guidelines for optimizing future data collection aimed at capturing a violin's unique sound signature.
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7117308093
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