This paper presents a system that complements the tuner functionality by evaluating the sound quality of a music performer in real-time. It consists of a software tool that computes a score of how well single notes are played with respect to a collection of reference sounds. To develop such a tool we first record a collection of single notes played by professional performers. Then, the collection is annotated by music teachers in terms of the performance quality of each individual sample. From the recorded samples, several audio features are extracted and a machine learning method is used to find the features that best described performance quality according to musician's annotations. An evaluation is carried out to assess the correlation between systems’ predictions and musicians’ criteria. Results show that the system can reasonably predict musicians’ annotations of performance quality.

Authors: Romani Picas, Oriol; Parra Rodriguez, Hector; Dabiri, Dara; Tokuda, Hiroshi; Hariya, Wataru; Oishi, Koji; Serra, Xavier
Affiliations: Universitat Pompeu Fabra, Barcelona, Spain; KORG Inc., Tokyo, Japan(See document for exact affiliation information.)
AES Convention: 138 (May 2015) Paper Number: 9350
Publication Date: May 6, 2015 Import into BibTeX
Subject: Semantic Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=17774

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