A Study on Neural Models for Target-Based Computer-Assisted Musical Orchestration
Carmine-Emanuele Cella, Luke Dzwonczyk, Alejandro Saldarriaga-Fuertes, Hongfu Liu, Hélène-Camille Crayencour · HAL (Le Centre pour la Communication Scientifique Directe) · 2020
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.