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Is it Violin or Viola? Classifying the Instruments’ Music Pieces using Descriptive Statistics

Published:16 March 2023Publication History
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Abstract

Classifying music pieces based on their instrument sounds is pivotal for analysis and application purposes. Given its importance, techniques using machine learning have been proposed to classify violin and viola music pieces. The violin and viola are two different instruments with three overlapping strings of the same notes, and it is challenging for ordinary people or even musicians to distinguish the sound produced by these instruments. However, the classification of musical instrument pieces was barely performed by prior research. To solve this problem, we propose a technique using descriptive statistics to reliably distinguish between violin and viola music pieces. Likewise, a similar technique on the basis of histogram is introduced alongside the main descriptive statistics approach. These approaches are derived based on the nature of the instruments’ strings and the range of their pieces. We also solve the problem in the current literature which divide the audio into segments for processing instead of managing the whole song. Thereby, we compile a dataset of recordings that comprises of violin and viola solo pieces from the Baroque, Classical, Romantic, and Modern eras. Experiment results suggest that our approach achieves high accuracy on solo pieces as compared to other methods with 0.97 accuracy on Baroque pieces.

REFERENCES

  1. [1] Agostini Giulio, Longari Maurizio, and Pollastri Emanuele. 2003. Musical instrument timbres classification with spectral features. EURASIP J. Adv. Signal Process 2003 (Jan.2003), 514. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Aucouturier Jean-Julien and Pachet François. 2002. Scaling up music playlist generation. In Proceedings of the 2002 IEEE International Conference on Multimedia and Expo, ICME 2002, Lausanne, Switzerland. August 26–29, 2002. Volume I. IEEE Computer Society, Lausanne, Switzerland, 105108. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Avci Kemal, Arican Murat, and Polat Kemal. 2018. Machine learning based classification of violin and viola instrument sounds for the same notes. In 26th Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, May 2–5, 2018. IEEE, Izmir, Turkey, 14. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Balen Jan Van, Burgoyne John Ashley, Wiering Frans, and Veltkamp Remco C.. 2013. An analysis of chorus features in popular song. In Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013, Curitiba, Brazil, November 4–8, 2013, Jr. Alceu de Souza Britto, Gouyon Fabien, and Dixon Simon (Eds.). ISMIR, Curitiba, Brazil, 107112. http://www.ppgia.pucpr.br/ismir2013/wp-content/uploads/2013/09/180_Paper.pdf.Google ScholarGoogle Scholar
  5. [5] Bartsch M. A. and Wakefield G. H.. 2005. Audio thumbnailing of popular music using chroma-based representations. IEEE Transactions on Multimedia 7, 1 (2005), 96104. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Bittner Rachel M., Salamon Justin, Tierney Mike, Mauch Matthias, Cannam Chris, and Bello Juan Pablo. 2014. MedleyDB: A multitrack dataset for annotation-intensive MIR research. In Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR 2014, Taipei, Taiwan, October 27–31, 2014, Wang Hsin-Min, Yang Yi-Hsuan, and Lee Jin Ha (Eds.). ISMIR, Taipei, Taiwan, 155160. http://www.terasoft.com.tw/conf/ismir2014/proceedings/T028_322_Paper.pdf.Google ScholarGoogle Scholar
  7. [7] Bosch Juan J., Janer Jordi, Fuhrmann Ferdinand, and Herrera Perfecto. 2012. A comparison of sound segregation techniques for predominant instrument recognition in musical audio signals. In Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012, Mosteiro S.Bento Da Vitória, Porto, Portugal, October 8-12, 2012, Gouyon Fabien, Herrera Perfecto, Martins Luis Gustavo, and Müller Meinard (Eds.). FEUP Edições, Porto, Portugal, 559564. http://ismir2012.ismir.net/event/papers/559-ismir-2012.pdf.Google ScholarGoogle Scholar
  8. [8] Brown J., Houix O., and McAdams S.. 2001. Feature dependence in the automatic identification of musical woodwind instruments. The Journal of the Acoustical Society of America 109 3 (2001), 1064–72.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Chou Szu-Yu, Jang Jyh-Shing Roger, and Yang Yi-Hsuan. 2018. Learning to recognize transient sound events using attentional supervision. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, Lang Jérôme (Ed.). ijcai.org, Stockholm, Sweden, 33363342. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Diment Aleksandr, Rajan Padmanabhan, Heittola Toni, and Virtanen Tuomas. 2013. Modified group delay feature for musical instrument recognition. In 10th International Symposium on Computer Music Multidisciplinary Research, 15- 18 October 2013, Marseille, France (International Symposium on Computer Music Multidisciplinary Research). LMA, Marseille, France, 431438. Google ScholarGoogle Scholar
  11. [11] Eronen A.. 2001. Comparison of features for musical instrument recognition. In Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics. IEEE, New Platz, NY, USA, 1922.Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] George Alan. 1995. Classical and romantic chamber music for strings. Early Music XXIII, 2 (051995), 341348. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Goto Masataka, Hashiguchi Hiroki, Nishimura Takuichi, and Oka Ryuichi. 2002. RWC music database: Popular, classical and jazz music databases. In ISMIR 2002, 3rd International Conference on Music Information Retrieval, Paris, France, October 13-17, 2002, Proceedings. ISMIR, Paris, France, 287288. http://ismir2002.ismir.net/proceedings/03-SP04-1.pdf.Google ScholarGoogle Scholar
  14. [14] Han Y., Kim J., and Lee K.. 2017. Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, 1 (2017), 208221.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. [15] Hung Yun-Ning and Yang Yi-Hsuan. 2018. Frame-level instrument recognition by timbre and pitch. In Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France, September 23-27, 2018, Gómez Emilia, Hu Xiao, Humphrey Eric, and Benetos Emmanouil (Eds.). ISMIR, Paris, France, 135142. http://ismir2018.ircam.fr/doc/pdfs/55_Paper.pdf.Google ScholarGoogle Scholar
  16. [16] Jolliffe Ian. 2011. Principal Component Analysis. Springer, Berlin, Berlin, 10941096. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Kaminskyj Ian Ihor and Czaszejko Tadeusz. 2005. Automatic recognition of isolated monophonic musical instrument sounds using kNNC. Journal of Intelligent Information Systems 24, 2/3 (2005), 199221. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Kostek B.. 2004. Musical instrument classification and duet analysis employing music information retrieval techniques. Proc. IEEE 92, 4 (2004), 712729.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Kurth Frank and Müller Meinard. 2008. Efficient index-based audio matching. Audio, Speech, and Language Processing, IEEE Transactions on 16 (032008), 382395.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] LeCun Yann, Bengio Y., and Hinton Geoffrey. 2015. Deep learning. Nature 521 (052015), 436–44. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Li Peter, Qian Jiyuan, and Wang Tian. 2015. Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks. (2015). arxiv:cs.SD/1511.05520Google ScholarGoogle Scholar
  22. [22] Lidy Thomas and Rauber Andreas. 2005. Evaluation of feature extractors and psycho-acoustic transformations for music genre classification. In ISMIR 2005, 6th International Conference on Music Information Retrieval, London, UK, 11-15 September 2005, Proceedings. ISMIR, London, UK, 3441.Google ScholarGoogle Scholar
  23. [23] Adamson Eric Durian, Matt Hallaron, and Scott. 1997. University of Iowa musical instrument samples. http://theremin.music.uiowa.edu/MIS.html.Google ScholarGoogle Scholar
  24. [24] McFee Brian, Raffel Colin, Liang Dawen, Ellis Daniel, McVicar Matt, Battenberg Eric, and Nieto Oriol. 2015. Librosa: Audio and music signal analysis in Python. In Proceedings of the 14th Python in Science Conference. SciPy, Austin, Texas, 1824. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] McKay Cory and Fujinaga Ichiro. 2006. Musical genre classification: Is it worth pursuing and how can it be improved? In ISMIR 2006, 7th International Conference on Music Information Retrieval, Victoria, Canada, 8–12 October 2006, Proceedings. ISMIR, Victoria, Canada, 101106.Google ScholarGoogle Scholar
  26. [26] Müller Meinard, Kurth Frank, and Clausen Michael. 2005. Audio matching via chroma-based statistical features. In ISMIR 2005, 6th International Conference on Music Information Retrieval, London, UK, 11-15 September 2005, Proceedings. ISMIR, London, UK, 288295.Google ScholarGoogle Scholar
  27. [27] Park Taejin and Lee Taejin. 2015. Musical instrument sound classification with deep convolutional neural network using feature fusion approach. (2015). arxiv:cs.SD/1512.07370Google ScholarGoogle Scholar
  28. [28] Parr Freya. 2018. What’s the difference between a violin and a viola? (2018). https://www.classical-music.com/features/articles/what-difference-between-violin-and-viola/.Google ScholarGoogle Scholar
  29. [29] Peeters Geoffroy, McAdams Stephen, and Herrera Perfecto. 2000. Instrument sound description in the context of MPEG-7. In ICMC: International Computer Music Conference, Berlin, Germany, 166169. https://hal.archives-ouvertes.fr/hal-01161319.cote interne IRCAM: Peeters00a.Google ScholarGoogle Scholar
  30. [30] Roche Elizabeth. 2019. Of myths and baroque music. Early Music 47, 1 (032019), 132135. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Subotnik Eva E.. 2015. Copyright and the living dead?: Succession law and the postmortem term. Estate Planning eJournal 29, 1 (2015), 8293.Google ScholarGoogle Scholar
  32. [32] Team StringOvation. 2018. The Romantic Period of Music. (2018). https://www.connollymusic.com/stringovation/the-romantic-period-of-music.Google ScholarGoogle Scholar
  33. [33] Team Western Michigan University. 2012. Modern Art Music. (2012). https://wmich.edu/mus-gened/mus150/1500.%20webbook%20modern%20artmusic/Modern%20ArtMusic.htm.Google ScholarGoogle Scholar
  34. [34] Tseng Yuen-Hsien. 1999. Content-based retrieval for music collections. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 176182. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Wang Qi and Bao Changchun. 2020. Individual violin recognition method combining tonal and nontonal features. Electronics 9, 6 (Jun.2020), 950. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Yu Yi, Tang Suhua, Raposo Francisco, and Chen Lei. 2019. Deep cross-modal correlation learning for audio and lyrics in music retrieval. ACM Trans. Multimedia Comput. Commun. Appl. 15, 1, Article 20 (Feb.2019), 16 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 2s
      April 2023
      545 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3572861
      • Editor:
      • Abdulmotaleb El Saddik
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 March 2023
      • Online AM: 14 September 2022
      • Accepted: 24 August 2022
      • Revised: 23 May 2022
      • Received: 1 December 2021
      Published in tomm Volume 19, Issue 2s

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