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Emotion-based music recommendation using supervised learning

Published: 30 November 2015 Publication History

Abstract

Music recommendation systems are well explored and commonly used but are normally based on manually tagged parameters and simple similarity calculation. Our project proposes a recommendation system based on emotional computing, automatic classification and feature extraction, which recommends music based on the emotion expressed by the song.
To achieve this goal a set of features is extracted from the song, including the MFCC (mel-frequency cepstral coefficients) following the works of McKinney et al. [6] and a machine learning system is trained on a set of 424 songs, which are categorized by emotion. The categorization of the song is performed manually by multiple persons to avoid error. The emotional categorization is performed using a modified version of the Tellegen-Watson-Clark emotion model [7], as proposed by Trohidis et al. [8]. The System is intended as desktop application that can reliably determine similarities between the main emotion in multiple pieces of music, allowing the user to choose music by emotion. We report our findings below.

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ZIP File (p341-bodarwe.zip)
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References

[1]
Doris Baum. 2006. EmoMusic -- Classifying Music According to Emotion. (2006). http://www.ifs.tuwien.ac.at/mir/pub/baum_wsom06.pdf
[2]
JungHyun Kim, Seungjae Lee, SungMin Kim, and WonYoung Yoo. 2011. Music mood classification model based on arousal-valence values. In Advanced Communication Technology (ICACT), 2011 13th International Conference on. IEEE, 292--295.
[3]
Youngmoo E Kim, Erik M Schmidt, Raymond Migneco, Brandon G Morton, Patrick Richardson, Jeffrey Scott, Jacquelin a Speck, and Douglas Turnbull. 2010. Music Emotion Recognition: a State of the Art Review. Information Retrieval Ismir (2010), 255--266. httpp://ismir2010.ismir.net/proceedings/ismir2010-45.pdf
[4]
Carol L Krumhansl. 2002. Music: A link between cognition and emotion. Current directions in psychological science 11, 2 (2002), 45--50.
[5]
Daniel McEnnis, Cory McKay, Ichiro Fujinaga, and Philippe Depalle. 2005. jAudio: A feature extraction library. In Proceedings of the International Conference on Music Information Retrieval. 600-603.
[6]
Mf Martin F Mckinney and Jeroen Breebaart. 2003. Features for Audio and Music Classification. In Proceedings of the International Conference on Music Information Retrieval, Vol. 4. 151-158.
[7]
A. Tellegen, D. Watson, and L. A. Clark. 1999. On the Dimensional and Hierarchical Structure of Affect. Psychological Science 10 (1999), 297-303. Issue 4.
[8]
Konstantinos Trohidis, Grigorios Tsoumakas, George Kalliris, and Ioannis Vlahavas. 2011. Multi-label classification of music by emotion. EURASIP Journal on Audio, Speech, and Music Processing 2011, 1 (2011), 4.
[9]
George Tzanetakis and Perry Cook. 2002. Musical genre classification of audio signals. In IEEE Transactions on Speech and Audio Processing, Vol. 10. 293-302.

Cited By

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  • (2023)Emotion Based Song Suggestion System for Tamil Language2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP57993.2023.10134792(1-6)Online publication date: 18-Mar-2023

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    Published In

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    MUM '15: Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia
    November 2015
    442 pages
    ISBN:9781450336055
    DOI:10.1145/2836041
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    • FH OOE: University of Applied Sciences Upper Austria
    • Johannes Kepler Univ Linz: Johannes Kepler Universität Linz

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 November 2015

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    Author Tags

    1. MFCC
    2. RMS
    3. chroma
    4. music emotion
    5. music recommendation
    6. naive bayes
    7. supervised learning

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    MUM '15
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    • FH OOE
    • Johannes Kepler Univ Linz

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    MUM '15 Paper Acceptance Rate 33 of 89 submissions, 37%;
    Overall Acceptance Rate 190 of 465 submissions, 41%

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    • (2023)Emotion Based Song Suggestion System for Tamil Language2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP57993.2023.10134792(1-6)Online publication date: 18-Mar-2023

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