Evaluation of Chord and Chroma Features and Dynamic Time Warping Scores on Cover Song Identification Task

  • Ladislav Maršík
  • Martin Rusek
  • Kateřina Slaninová
  • Jan Martinovič
  • Jaroslav Pokorný
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10244)

Abstract

Cover song identification has been a popular task within music information retrieval in the 20th century. The task is to identify a different version or performance of a previously recorded song. Unlike audio search for an exact matching song, this task has not yet been popularized among users, due to an ambiguous definition of a cover song and the complexity of the problem. With a great variety of methods proposed on the benchmarking challenges, it is increasingly difficult to compare advantages and disadvantages of the features and algorithms. We provide a comparison of three levels of feature extraction (chroma features, chroma vector distances, chord distances) and show how each level affects the results. We further distinguish five scores for dynamic time warping method, to find the best performance in conjunction with the features. Results were evaluated on covers80 and SecondHandSongs datasets and compared to the state-of-the-art.

Keywords

Music information retrieval Chroma features Chord distance Chroma vector distance Cover song identification Dynamic time warping 

Notes

Acknowledgments

This work has been partially funded by the Charles University, project GA UK No. 1580317, project SVV 260451, by grant of SGS No. SP2017/177 “Optimization of machine learning algorithms for the HPC platform”, VŠB - Technical University of Ostrava, Czech Republic, by The Ministry of Education, Youth and Sports of the Czech Republic from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science - LQ1602” and by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center - LM2015070”.

References

  1. 1.
    Bartsch, M.A., Wakefield, G.H.: To catch a chorus: using chroma-based representations for audio thumbnailing. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2001 (2001)Google Scholar
  2. 2.
    Bello, J.P.: Audio-based cover song retrieval using approximate chord sequences: testing shifts, gaps, swaps and beats. In: Music Information Retrieval Evaluation eXchange, MIREX 2007 (2007)Google Scholar
  3. 3.
    Bertin-Mahieux, T., Ellis, D.P.W.: Large-scale cover song recognition using hashed chroma landmarks. In: IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics, WASPAA 2011. IEEE (2011)Google Scholar
  4. 4.
    Bertin-Mahieux, T., Ellis, D.P.: Large-scale cover song recognition using the 2D fourier transform magnitude. In: Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012 (2012)Google Scholar
  5. 5.
    Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P.: The million song dataset. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011 (2011)Google Scholar
  6. 6.
    De Haas, W.B., Veltkamp, R., Wiering, F.: Tonal pitch step distance: a similarity measure for chord progressions. In: Proceedings of the 9th International Conference on Music Information Retrieval, ISMIR 2008 (2008)Google Scholar
  7. 7.
    Ellis, D.P.W., Poliner, G.E.: Identifying ‘Cover Songs’ with chroma features and dynamic programming beat tracking. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2007 (2007)Google Scholar
  8. 8.
    Ellis, D.P.W.: Identifying ‘Cover Songs’ with beat-synchronous chroma features. In: Music Information Retrieval Evaluation eXchange, MIREX 2006 (2006)Google Scholar
  9. 9.
    Ellis, D.P.W., Cotton, C.V.: The 2007 LabROSA Cover Song Detection System. In: Music Information Retrieval Evaluation eXchange, MIREX 2007 (2007)Google Scholar
  10. 10.
    Foster, P., Dixon, S., Klapuri, A.: Identifying cover songs using information-theoretic measures of similarity. IEEE/ACM Trans. Audio Speech Lang. Process. 23(6), 993–1005 (2015)CrossRefGoogle Scholar
  11. 11.
    Fujishima, T.: Realtime chord recognition of musical sound: a system using common lisp music. In: Proceedings of the International Computer Music Conference, ICMC 1999 (1999)Google Scholar
  12. 12.
    Gómez, E.: Tonal description of music audio signals, Ph.D. thesis, Universitat Pompeu Fabra (2006)Google Scholar
  13. 13.
    Khadkevich, M., Omologo, M.: Large-scale cover song identification using chord profiles. In: Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013 (2013)Google Scholar
  14. 14.
    Kocyan, T.: Adapting case-based reasoning for processing natural phenomena data, Ph.D. thesis, VŠB Technical University of Ostrava (2015)Google Scholar
  15. 15.
    Lee, K.: Identifying cover songs from audio using harmonic representation. In: Music Information Retrieval Evaluation eXchange, MIREX 2006 (2006)Google Scholar
  16. 16.
    Marsik, L., Pokorny, J., Ilcik, M.: Towards a harmonic complexity of musical pieces. In: Proceedings of the 14th Annual International Workshop on Databases, Texts, Specifications and Objects (DATESO 2014), CEUR Workshop Proceedings, vol. 1139 (2014). CEUR-WS.org
  17. 17.
    Mueen, A., Keogh, E.J.: Extracting optimal performance from dynamic time warping, KDD 2016 (2016)Google Scholar
  18. 18.
    Müller, M.: Information Retrieval for Music and Motion. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Osmalskyj, J., Piérard, S., Van Droogenbroeck, M., Embrechts, J.J.: Efficient database pruning for large-scale cover song recognition. In: International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 (2013)Google Scholar
  20. 20.
    Robine, M., Hanna, P., Ferraro, P., Allali, J.: Adaptation of string matching algorithms for identification of near-duplicate music documents. In: Proceedings of the International SIGIR Workshop on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection, SIGIR-PAN 2007 (2007)Google Scholar
  21. 21.
    Rocher, T., Robine, M., Hanna, P., Desainte-Catherine, M.: A survey of chord distances with comparison for chord analysis. In: Proceedings of the International Computer Music Conference, ICMC 2010 (2010)Google Scholar
  22. 22.
    Serrà, J., Gómez, E., Herrera, P., Serra, X.: Chroma binary similarity and local alignment applied to cover song identification. IEEE Trans. Audio Speech Lang. Process. 16, 1138–1152 (2008)CrossRefGoogle Scholar
  23. 23.
    Serrà, J., Serra, X., Andrzejak, R.G.: Cross recurrence quantification for cover song identification. New J. Phys. 11(9), 093017 (2009)CrossRefGoogle Scholar
  24. 24.
    Tralie, C.J., Bendich, P.: Cover song identification with timbral shape sequences. In: Music Information Retrieval Evaluation eXchange, MIREX 2015 (2015)Google Scholar
  25. 25.
    Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multidimensional time-series. VLDB J. 15(1), 1–20 (2006)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Ladislav Maršík
    • 3
  • Martin Rusek
    • 1
  • Kateřina Slaninová
    • 1
    • 2
  • Jan Martinovič
    • 1
    • 2
  • Jaroslav Pokorný
    • 3
  1. 1.IT4InnovationsVŠB - Technical University of OstravaOstravaCzech Republic
  2. 2.Department of Computer Science, FEECSVŠB - Technical University of OstravaOstrava-PorubaCzech Republic
  3. 3.Department of Software Engineering, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

Personalised recommendations