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Automatically Determining the Popularity of a Song

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

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Abstract

The purpose of this paper is to identify a connection, if such a connection exists, between the sequence of sounds and the lyrics of a melody and its popularity with the help of machine learning techniques. The melody popularity will be quantified as the number of views and number of “like” votes on the YouTube platform, where users can upload, view and vote videos. This analysis will reveal whether the two indicators from the YouTube platform are more influenced by the words or sounds of the songs. This work may help the producers from the music industry since the popularity of a melody (determined by analyzing a large set of songs) might be a very important aspect to be considered when deciding whether to make and launch a musical product or not.

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Notes

  1. 1.

    https://www.youtube.com/yt/press/en-GB/statistics.html [accessed: February 2nd, 2017].

  2. 2.

    http://www.scoreahit.com [accessed: February 2nd, 2017].

  3. 3.

    http://polyphonichmi.blogspot.ro/p/about-company.html [accessed: February 2nd, 2017].

  4. 4.

    https://en.wikipedia.org/wiki/List_of_dance-pop_artists [accessed: February 2nd, 2017].

  5. 5.

    http://www.lyricsmania.com/ [accessed: February 2nd, 2017].

  6. 6.

    https://pythonhosted.org/pyenchant/ [accessed: February 2nd, 2017].

  7. 7.

    http://www.fon.hum.uva.nl/praat/manual/sampling_frequency.html [accessed: February 2nd, 2017].

  8. 8.

    http://www.mega-nerd.com/libsndfile/ [accessed: February 2nd, 2017].

  9. 9.

    http://www.fftw.org [accessed: February 2nd, 2017].

  10. 10.

    http://scikit-learn.org/ [accessed: February 2nd, 2017].

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Acknowledgments

This work has been funded by University Politehnica of Bucharest, through the “Excellence Research Grants” Program, UPB – GEX. Identifier: UPB–EXCELENȚĂ–2016 Aplicareametodelor de învățareautomatăînanalizaseriilor de timp (Applying machine learning techniques in time series analysis), Contract number 09/26.09.2016.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University Politehnica of Bucharest, 313 Splaiul Independentei, Bucharest, Romania

    Costin Chiru & Oana-Georgiana Popescu

Authors
  1. Costin Chiru
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  2. Oana-Georgiana Popescu
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Corresponding author

Correspondence to Costin Chiru .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Chiru, C., Popescu, OG. (2017). Automatically Determining the Popularity of a Song. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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