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Music has been commonly recognized as a means of expressing emotions. In thissense, an intense debate emerges from the need to verbalize musical emotions.This concern seems highly relevant today, considering the exponential growth ofnatural language processing using deep learning models where it is possible toprompt semantic propositions to generate music automatically. This scopingreview aims to analyze and discuss the possibilities of music generationconditioned by emotions. To address this topic, we propose a historicalperspective that encompasses the different disciplines and methods contributingto this topic. In detail, we review two main paradigms adopted in automaticmusic generation: rules-based and machine-learning models. Of note are the deeplearning architectures that aim to generate high-fidelity music from textualdescriptions. These models raise fundamental questions about the expressivityof music, including whether emotions can be represented with words or expressedthrough them. We conclude that overcoming the limitation and ambiguity oflanguage to express emotions through music, some of the use of deep learningwith natural language has the potential to impact the creative industries byproviding powerful tools to prompt and generate new musical works.

Are Words Enough? On the semantic conditioning of affective music generation