Abstract
Managing the emotional aspect remains a challenge in automatic music generation. Prior works aim to learn various emotions at once, leading to inadequate modeling. This paper explores the disentanglement of emotions in piano performance generation through a two-stage framework. The first stage focuses on valence modeling of lead sheet, and the second stage addresses arousal modeling by introducing performance-level attributes. To further capture features that shape valence, an aspect less explored by previous approaches, we introduce a novel functional representation of symbolic music. This representation aims to capture the emotional impact of major-minor tonality, as well as the interactions among notes, chords, and key signatures. Objective and subjective experiments validate the effectiveness of our framework in both emotional valence and arousal modeling. We further leverage our framework in a novel application of emotional controls, showing a broad potential in emotion-driven music generation.
Abstract (translated)
管理情感方面仍然是一个自动音乐生成的挑战。先前的作品试图同时学习各种情感,导致模型不足。本文通过两个阶段的框架探讨了钢琴演奏生成中情感的解耦。第一阶段集中在主旋律模型的情感度量,第二阶段通过引入表现级别属性来解决情感建模。为了更好地捕捉塑造情感的特征,我们引入了一种新的符号音乐功能表示。这种表示旨在捕捉大调小调音阶的情感影响,以及音符、和弦和音高的相互作用。客观和主观实验证实了我们在情感值和情感建模方面的框架的有效性。我们还在一个新的情感控制应用中利用了我们的框架,展示了在情感驱动音乐生成方面广泛的应用潜力。
URL
https://arxiv.org/abs/2407.20955
