An Evaluation of Score Descriptors Combined with Non-linear Models of Expressive Dynamics in Music
| Autor: | Maarten Grachten, Carlos Eduardo Cancino Chacón |
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| Rok vydání: | 2015 |
| Předmět: |
Interpretation (logic)
Artificial neural network Computer science business.industry Linear model Musical expression computer.software_genre Machine learning Set (abstract data type) Computer Science::Sound Simple (abstract algebra) Dynamics (music) Artificial intelligence Simple linear regression business computer Natural language processing |
| Zdroj: | Discovery Science ISBN: 9783319242811 Discovery Science |
| DOI: | 10.1007/978-3-319-24282-8_6 |
| Popis: | Expressive interpretation forms an important but complex aspect of music, in particular in certain forms of classical music. Modeling the relation between musical expression and structural aspects of the score being performed, is an ongoing line of research. Prior work has shown that some simple numerical descriptors of the score (capturing dynamics annotations and pitch) are effective for predicting expressive dynamics in classical piano performances. Nevertheless, the features have only been tested in a very simple linear regression model. In this work, we explore the potential of a non-linear model for predicting expressive dynamics. Using a set of descriptors that capture different types of structure in the musical score, we compare the predictive accuracies of linear and non-linear models. We show that, in addition to being (slightly) more accurate, non-linear models can better describe certain interactions between numerical descriptors than linear models. |
| Databáze: | OpenAIRE |
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