Automatic Assessment of Tone Quality in Violin Music Performance
| Autor: | Aaron Williamon, Oscar Mayor, Rafael Ramirez, Ariadna Ortega, Alfonso Perez, Sergio Giraldo, George Waddell, Ignasi Nou |
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| Jazyk: | angličtina |
| Předmět: |
media_common.quotation_subject
Speech recognition lcsh:BF1-990 050105 experimental psychology Violin 03 medical and health sciences Tone (musical instrument) 0302 clinical medicine musaes Machine learning Feature (machine learning) Psychology 0501 psychology and cognitive sciences Quality (business) Sound quality Violin performance General Psychology media_common Original Research mupsy Music psychology Automatic assessment of music 05 social sciences Performance science Tone quality Music performance lcsh:Psychology PerfSci Timbre 030217 neurology & neurosurgery |
| Zdroj: | Frontiers in Psychology, Vol 10 (2019) Frontiers in Psychology Recercat. Dipósit de la Recerca de Catalunya instname |
| ISSN: | 1664-1078 |
| DOI: | 10.3389/fpsyg.2019.00334 |
| Popis: | The automatic assessment of music performance has become an area of increasing interest due to the growing number of technology-enhanced music learning systems. In most of these systems, the assessment of musical performance is based on pitch and onset accuracy, but very few pay attention to other important aspects of performance, such as sound quality or timbre. This is particularly true in violin education, where the quality of timbre plays a significant role in the assessment of musical performances. However, obtaining quantifiable criteria for the assessment of timbre quality is challenging, as it relies on consensus among the subjective interpretations of experts. We present an approach to assess the quality of timbre in violin performances using machine learning techniques. We collected audio recordings of several tone qualities and performed perceptual tests to find correlations among different timbre dimensions. We processed the audio recordings to extract acoustic features for training tone-quality models. Correlations among the extracted features were analyzed and feature information for discriminating different timbre qualities were investigated. A real-time feedback system designed for pedagogical use was implemented in which users can train their own timbre models to assess and receive feedback on their performances. This work has been partly sponsored by the Spanish TIN project TIMUL (TIN2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). |
| Databáze: | OpenAIRE |
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