Neural Models for Target-Based Computer-Assisted Musical Orchestration: A Preliminary Study

Abstract

In this paper we will perform a preliminary exploration on how neural networks can be used for the task of target-based computer-assisted 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. This paper is an extended version of the paper "A Study on Neural Models for Target-Based Computer-Assisted Musical Orchestration" published in the proceedings of The 2020 Joint Conference on AI Music Creativity

Keywords

Orchidea, ResNet, LSTM, CNN, computer-assisted orchestration

How to Cite

Cella, C. E., Dzwonczyk, L. J., Saldarriaga-Fuertes, A., Liu, H. & Crayencour, H., (2022) “Neural Models for Target-Based Computer-Assisted Musical Orchestration: A Preliminary Study”, Journal of Creative Music Systems 1(1). doi: https://doi.org/10.5920/jcms.890

488

Views

328

Downloads

Share

Authors

Carmine Emanuele Cella (University of California, Berkeley)
Luke Jonathan Dzwonczyk (University of California, Berkeley)
Alejandro Saldarriaga-Fuertes (University of California, Berkeley)
Hongfu Liu (University of California, Berkeley)
Hélène-Camille Crayencour (University of Paris-Saclay)

Download

Issue

Dates

Licence

Creative Commons Attribution 4.0

Identifiers

Peer Review

This article has been peer reviewed.

File Checksums (MD5)

  • Post-Copyedit pdf: 81c2f4ca6e8793fd810d42ddbc3dc822