Skip to main content
SpringerLink
Log in

Creating a Reliable Music Discovery and Recommendation System

  • Chapter
  • First Online:
Intelligent Tools for Building a Scientific Information Platform: From Research to Implementation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 541))

Abstract

The aim of this chapter is to show problems related to creating a reliable music discovery system. The SYNAT database that contains audio files is used for the purpose of experiments. The files are divided into 22 classes corresponding to music genres with different cardinality. Of utmost importance for a reliable music recommendation system are the assignment of audio files to their appropriate genres and optimum parameterization for music-genre recognition. Hence, the starting point is audio file filtering, which can only be done automatically, but to a limited extent, when based on low-level signal processing features. Therefore, a variety of parameterization techniques are shortly reviewed in the context of their suitability to music retrieval from a large music database. In addition, some significant problems related to choosing an excerpt of audio file for an acoustic analysis and parameterization are pointed out. Then, experiments showing results of searching for songs that bear the greatest resemblance to the song in a given query are presented. In this way music recommendation system may be created that enables to retrieve songs that are similar to each other in terms of their low-level feature description and genre inclusion. The experiments performed also provide basis for more general observations and conclusions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aucouturier, J.-J., Pachet, F.: Representing musical genre: a state of art. J. New Music Res. str. 32, 83–93 (2003)

    Google Scholar 

  2. Benesty, J., Mohan Sondhi, M., Huang, Y.: Springer Handbook of Speech Processing. Springer, Heidelberg (2008)

    Google Scholar 

  3. Bello, J.P.: Low-level features and timbre, MPATE-GE 2623 music information retrieval. New York University. http://www.nyu.edu/classes/bello/MIR_files/timbre.pdf

  4. Ewert, S.: Signal processing methods for music, synchronization, audio matching, and source separation, Bonn (2012)

    Google Scholar 

  5. Holzapfel, A., Stylianou, Y.: Musical genre classification using nonnegative matrix factorization-based features. IEEE Trans. Audio Speech Lang. Process. 16(2), 424–434 (2008)

    Article  Google Scholar 

  6. Hyoung-Gook, K., Moreau, N., Sikora, T.: MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval. Wiley, New York (2005)

    Google Scholar 

  7. Jang, D., Jin, M., Yoo, C.D.: Music genre classification using novel features and a weighted voting method. In: ICME, pp. 1377–1380 (2008)

    Google Scholar 

  8. Kilem, G.: Inter-Rater Reliability: Dependency on Trait Prevalence and Marginal Homogeneity, Statistical Methods For Inter-Rater Reliability Assessment, No. 2, (2002)

    Google Scholar 

  9. Kostek, B.: Content-Based Approach to Automatic Recommendation of Music, 131 Audio Engineering Convention. New York (2011)

    Google Scholar 

  10. Kostek, B.: Music information retrieval in music repositories. In: Suraj, Z., Skowron, A. (eds.) Chapter in Intelligent Systems Reference Library, pp. 459–485. Springer, Berlin (2012)

    Google Scholar 

  11. Kostek, B.: Music information retrieval in music repositories. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems, pp. 463–489. Springer, Berlin 2013

    Google Scholar 

  12. Kostek, B.: Perception-Based Data Processing in Acoustics, Applications to Music Information Retrieval and Psychophysiology of Hearing, Series on Cognitive Technologies. Springer, Berlin (2005)

    Google Scholar 

  13. Kostek B.: Soft computing in acoustics, applications of neural networks, fuzzy logic and rough sets to musical acoustics. In: Studies in Fuzziness and Soft Computing, Physica, New York (1999)

    Google Scholar 

  14. Kostek, B., Czyzewski, A.: Representing musical instrument sounds for their automatic classification. J. Audio Eng. Soc. 49, 768–785 (2001)

    Google Scholar 

  15. Kostek, B., Kania, Ł.: Music information analysis and retrieval techniques. Arch. Acoust. Str. 33(4), 483–496 (2008)

    Google Scholar 

  16. Kostek, B., Kupryjanow, A., Zwan, P., Jiang, W., Ras, Z., Wojnarski, M., Swietlicka, J.: Report of the ISMIS 2011 Contest: Music Information Retrieval, Foundations of Intelligent Systems, ISMIS 2011, pp. 715–724. Springer, Berlin (2011)

    Google Scholar 

  17. Lastfm: http://www.last.fm/

  18. Li, T., Ogihara, M., Li, Q.: A comparative study on content-based music genre classification. In: 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, str. pp. 282–289. Toronto, Canada (2003)

    Google Scholar 

  19. Lidy, T., Rauber, A., Pertusa, A., Inesta, J.: Combining audio and symbolic descriptors for music classification from audio. In: Music Information Retrieval Information Exchange (MIREX) (2007)

    Google Scholar 

  20. Lindsay, A., Herre J.: MPEG-7 and MPEG-7 audio - an overview. J. Audio Eng. Soc. Str. 49(7/8), 589–594 (2001)

    Google Scholar 

  21. Mandel, M., Ellis, D.: LABROSA’s audio music similarity and classification submissions, Austrian Computer Society, Columbia University, LabROSA (2007)

    Google Scholar 

  22. Music store Amazon: http://www.amazon.com/

  23. Music store Itunes: https://www.apple.com/pl/itunes/

  24. Panagakis, E., Benetos, E., Kotropoulos, C.: Music genre classification: a multilinear approach. In: Proceedings of ISMIR, pp. 583–588 (2008)

    Google Scholar 

  25. Pandora: http://www.pandora.com

  26. Shlens, J.: A tutorial on principal component analysis, Salk Insitute for Biological Studies La Jolla, New York (2005)

    Google Scholar 

  27. Symeonidis, P., Ruxanda, P., Nanopoulos, A., Manolopoulos, Y.: Ternary semantic analysis of social tags for personalized music recommendation. In: 9th International Conference on Music Information Retrieval str., pp. 219–224 (2008)

    Google Scholar 

  28. The International Society for Music Information Retrieval/Intern. Conference on Music Information Retrieval, website http://www.ismir.net/

  29. Tzanetakis, G., Cook, P.: Musical genre classification of audio signal. In: IEEE Transactions on Speech and Audio Processing Str., pp. 293–302 (2002)

    Google Scholar 

  30. Tzanetakis, G., Essl, G., Cook, P.: Automatic musical genre classification of audio signals. In: Proceedings of International Symposium on Music Information Retrieval (ISMIR) (2001)

    Google Scholar 

  31. Żwan, P., Kostek, B.: System for automatic singing voice recognition. J. Audio Eng. Soc. 56(9), 710–723 (2008)

    Google Scholar 

Download references

Acknowledgments

This research was conducted and partially founded within project No. SP/I/1/77065/10, ‘The creation of a universal, open, repository platform for the hosting and communication of networked knowledge resources for science, education and an open knowledge society’, which is part of the Strategic Research Program, ‘Interdisciplinary systems of interactive scientific and technical information’ supported by the National Centre for Research and Development (NCBiR) in Poland.

The authors are very grateful to the reviewers for their comments and suggestions.

Author information

Authors and Affiliations

  1. Audio Acoustics Laboratory, Gdańsk University of Technology, 80-233, Gdańsk, Poland

    Bożena Kostek

  2. Multimedia Systems Department, Gdańsk University of Technology, 80-233, Gdańsk, Poland

    Piotr Hoffmann, Andrzej Kaczmarek & Paweł Spaleniak

Authors
  1. Bożena Kostek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrzej Kaczmarek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paweł Spaleniak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Bożena Kostek or Paweł Spaleniak .

Editor information

Editors and Affiliations

  1. Faculty of Electronics and Information Technology, Warsaw University of Technology, Institute of Computer Science, Warsaw, Poland

    Robert Bembenik

  2. Faculty of Electronics and Information Technology, Warsaw University of Technology, Institute of Computer Science, Warsaw, Poland

    Łukasz Skonieczny

  3. Faculty of Electronics and Information Technology, Warsaw University of Technology, Institute of Computer Science, Warsaw, Poland

    Henryk Rybiński

  4. Faculty of Electronics and Information Technology, Warsaw University of Technology, Institute of Computer Science, Warsaw, Poland

    Marzena Kryszkiewicz

  5. InterdisciplinaryCentre for Mathematical and Computational Modelling (ICM), University of Warsaw, Warsaw, Poland

    Marek Niezgódka

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kostek, B., Hoffmann, P., Kaczmarek, A., Spaleniak, P. (2014). Creating a Reliable Music Discovery and Recommendation System. In: Bembenik, R., Skonieczny, Ł., Rybiński, H., Kryszkiewicz, M., Niezgódka, M. (eds) Intelligent Tools for Building a Scientific Information Platform: From Research to Implementation. Studies in Computational Intelligence, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-04714-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04714-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04713-3

  • Online ISBN: 978-3-319-04714-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation