Implementation of Emotion in Music Composing: Evidence of Sadness, Happiness and Calmness

Zeen Li

2024

Abstract

As a matter of fact, emotion plays a crucial role in music creation, influencing how listeners perceive and react to musical works. With the advancement of artificial intelligence (especially deep learning), generating music that can convey specific emotions such as sadness, happiness, and calmness has become increasingly complex. This study explores the implementation of emotional expression in AI music creation, utilizing models such as long short-term memory (LSTM) networks, generative adversarial networks (GANs), and transformer-based architectures. This study analyses the ability of these models to generate emotionally resonant music and evaluate the results using quantitative/objective/algorithmic-analysis metrics (e.g., note density, harmonic content) and qualitative/subjective/human-cantered evaluations from human listeners. The results show that while these models can successfully produce music that matches the desired emotional characteristics, their effectiveness varies depending on the model and the target emotion. For example, GANs are particularly effective in generating happy music with unique rhythmic patterns, while Transformers master creating calm, coherent pieces. This study highlights the potential of AI for emotionally adaptive music applications, with important implications for areas such as therapeutic practice, interactive media, and personalized learning. Future work will focus on improving model accuracy and exploring cross-cultural emotional interpretation in music generation.

Paper Citation

in Harvard Style

Li Z. (2024). Implementation of Emotion in Music Composing: Evidence of Sadness, Happiness and Calmness. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 179-183. DOI: 10.5220/0013512200004619

in Bibtex Style

@conference{daml24,
author={Zeen Li},
title={Implementation of Emotion in Music Composing: Evidence of Sadness, Happiness and Calmness},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={179-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013512200004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Implementation of Emotion in Music Composing: Evidence of Sadness, Happiness and Calmness
SN - 978-989-758-754-2
AU - Li Z.
PY - 2024
SP - 179
EP - 183
DO - 10.5220/0013512200004619
PB - SciTePress