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Published on 6 June 2024
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Shen,F. (2024). Analysis of the realization for emotion recognition based on machine learning. Applied and Computational Engineering,68,137-142.
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Analysis of the realization for emotion recognition based on machine learning

Fuhong Shen *,1,
  • 1 The Affiliated High School to Hangzhou Normal University, Hangzhou, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/68/20241415

Abstract

As a matter of fact, music emotion recognition based on machine learning has been a trend in recent years. This study describes the analysis of the realization for emotion recognition based on machine learning. In this paper, the study expresses some methods of analyzing the emotion of the music composition. The paper represents some classifier molds that enable us to determine the emotion of the music. It aims to provide readers with a deeper comprehension of the music and composers’ feelings and to know more about the background of the music. The study may let more people to improve their own ability to appreciate music, if this process has let great amounts of individuals known. It also allows people to have more awareness and feelings with more voices. In this paper, the research will also show some about the evolution and original of the synthesizer. Moreover, the study will go to illustrate this through scientific, real examples and personal experiences and pictures.

Keywords

Emotion, music, machine learning

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Cite this article

Shen,F. (2024). Analysis of the realization for emotion recognition based on machine learning. Applied and Computational Engineering,68,137-142.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/
ISBN:978-1-83558-457-6(Print) / 978-1-83558-458-3(Online)
Conference date: 12 September 2024
Editor:Alan Wang, Roman Bauer
Series: Applied and Computational Engineering
Volume number: Vol.68
ISSN:2755-2721(Print) / 2755-273X(Online)

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