A Study on Neural Models for Target-Based Computer-Assisted Musical Orchestration
| Autor: | Cella, Carmine, Dzwonczyk, Luke, Saldarriaga-Fuertes, Alejandro, Liu, Hongfu, Crayencour, Helene-Camille |
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| Přispěvatelé: | Center for New Music and Audio Technologies (CNMAT), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Crayencour, Helene-Camille |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | 2020 Joint Conference on AI Music Creativity (CSMC + MuMe) 2020 Joint Conference on AI Music Creativity (CSMC + MuMe), Oct 2020, Stockholm, Sweden |
| Popis: | In this paper we will perform a preliminary exploration on how neural networks can be used for the task of target-based computerassisted musical orchestration. We will show how it is possible to model this musical problem as a classification task and we will propose two deep learning models. We will show, first, how they perform as classifiers for musical instrument recognition by comparing them with specific baselines. We will then show how they perform, both qualitatively and quantitatively, in the task of computer-assisted orchestration by comparing them with state-of-the-art systems. Finally, we will highlight benefits and problems of neural approaches for assisted orchestration and we will propose possible future steps. |
| Databáze: | OpenAIRE |
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