Subjective Evaluation of Deep Learning Models for Symbolic Music Composition

Autor:	Hernandez-Olivan, Carlos, Puyuelo, Jorge Abadias, Beltran, Jose R.
Rok vydání:	2022
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Deep learning models are typically evaluated to measure and compare their performance on a given task. The metrics that are commonly used to evaluate these models are standard metrics that are used for different tasks. In the field of music composition or generation, the standard metrics used in other fields have no clear meaning in terms of music theory. In this paper, we propose a subjective method to evaluate AI-based music composition systems by asking questions related to basic music principles to different levels of users based on their musical experience and knowledge. We use this method to compare state-of-the-art models for music composition with deep learning. We give the results of this evaluation method and we compare the responses of each user level for each evaluated model. Comment: Workshop on Generative AI and HCI, CHI 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.14641 Zobrazit plný text záznamu View this record from Arxiv