Review on music emotion analysis using machine learning: technologies, methods, datasets, and challenges
Abstract In recent years, the language of emotion has attracted widespread attention in music therapy. Each piece of music inherently carries an emotional essence, making the study of music emotion crucial. This work begins by exploring the historical background of music emotion and then conducts a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07178-9 |
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| Summary: | Abstract In recent years, the language of emotion has attracted widespread attention in music therapy. Each piece of music inherently carries an emotional essence, making the study of music emotion crucial. This work begins by exploring the historical background of music emotion and then conducts a comprehensive survey of existing machine learning, deep learning, and ensemble techniques employed in music emotion detection, along with a detailed analysis of the datasets utilized in these methodologies. Additionally, we identify and discuss the key issues and challenges emerging from our survey. Our work also delves into the contemporary technology of music emotion detection, focusing specifically on electroencephalography (EEG) signals. Lastly, we outline potential avenues for future research in music emotion detection. |
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| ISSN: | 3004-9261 |