Applications of deep neural networks on music emotion recognition

Yi Du

doi:10.54254/2755-2721/5/20230588

Research Article

Open access

Published on 30 May 2023

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Du,Y. (2023). Applications of deep neural networks on music emotion recognition. Applied and Computational Engineering,5,316-322.

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Applications of deep neural networks on music emotion recognition

Yi Du *^,1,

¹ Boston University, Boston, MA, 02215,United States

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/5/20230588

Abstract

Music Emotion Recognition (MER) is a subfield of Music Information Retrieval (MIR) that focuses on finding a relationship between music and human emotions by applying machine learning and signal processing techniques. In recent years, neural networks have achieved great success in a large number of areas, such as speech recognition and image processing, sparking many attempts to utilize neural networks in the MER task. Although new models for MER are constantly emerging, there are few systematic reviews in this field that involves the latest models and datasets. Therefore, in this paper, we provide a detailed review of this task. Our work first expounds on the practical significance and research status of the MER problem. Then, we encapsulate the background research and contributions of predecessors in both machine learning and psychology fields. Our work also includes a thorough analysis of several important datasets and their mathematical principles. Finally, we summarize four novel models from an application perspective and conclude a few potential challenges for the task.

Keywords

Music Emotion Recognition, Neural Networks, Nature Language Processing

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References

[1]. Loreto Parisi, Simone Francia, Silvio Olivastri, Maria Stella Tavella. Exploiting Synchronized Lyrics And Vocal Features For Music Emotion Detection. arXiv:1901.04831, 2019.

[2]. Jacek Grekow. Music emotion recognition using recurrent neural networks and pretrained models. Journal of Intelligent Information Systems (2021) 57:531–546.

[3]. Richard Orjesek, Roman Jarina, Michal Chmulik. End to end music emotion variation detection using iteratively reconstructed deep features. Multimedia Tools and Applications (2022) 81:5017–5031.

[4]. Serhat Hizlisoy, Serdar Yildirim, Zekeriya Tufekci. Music emotion recognition using convolutional long short term memory deep neural networks. Engineering Science and Technology, an International Journal 24 (2021) 760–767.

[5]. DEAM dataset - The MediaEval database for emotional analysis of music. http://cvml.unige.ch/datab ases/DEAM. Accessed 30 Sept 2020

[6]. Fika Hastarita Rachman, Riyanarto Sarno, Chastine Fatichah. Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion. International Journal of Electrical and Computer Engineering (IJECE), Vol. 8, No. 3, June 2018, pp. 1720~1730.

[7]. Russell, J. A. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178, 1980.

[8]. K. Hevner. Experimental studies of the elements of expression in music. The American Journal of Psychology, pages 246–268, 1936.

[9]. Gers, F. A., Schmidhuber, J., & Cummins, F.A. Learning to forget: Continual prediction with LSTM. Neural Computation, 12, 2451–2471, 2000.

[10]. Witten, I. H., Frank, E., Hall, M. A., & Pal, C.J. Data mining fourth edition: Practical machine learning tools and techniques, 4th edn. USA: Morgan Kaufmann Publishers Inc, 2016.

Cite this article

Du,Y. (2023). Applications of deep neural networks on music emotion recognition. Applied and Computational Engineering,5,316-322.

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 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.5

ISSN：2755-2721(Print) / 2755-273X(Online)

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