Software in Music Information Retrieval

  • Claus Weihs
  • Klaus Friedrichs
  • Markus Eichhoff
  • Igor Vatolkin
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Music Information Retrieval (MIR) software is often applied for the identification of rules classifying audio music pieces into certain categories, like e.g. genres. In this paper we compare the abilities of six MIR software packages in ten categories, namely operating systems, user interface, music data input, feature generation, feature formats, transformations and features, data analysis methods, visualization methods, evaluation methods, and further development. The overall rankings are derived from the estimated scores for the analyzed criteria.


Linear Discriminant Analysis Audio Signal Independent Component Analysis Music Piece Music Information Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Amatriain X (2007) CLAM: A framework for audio and music application development. IEEE Softw 24(1):82–85CrossRefGoogle Scholar
  2. Bischl B, Vatolkin I, Preuss M (2010) Selecting small audio feature sets in music classification by means of asymmetric mutation. In: PPSN XI: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature, Springer, Lecture Notes in Computer Science, vol 6238Google Scholar
  3. Lartillot O, Toiviainen P (2007) MIR in Matlab (II): A toolbox for musical feature extraction from audio. In: Proc. 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, pp 127–130Google Scholar
  4. McKay C (2010) Automatic music classification with jMIR. PhD thesis, McGill UniversityGoogle Scholar
  5. Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) YALE: Rapid prototyping for complex data mining tasks. In: KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, NY, pp 935–940Google Scholar
  6. Mörchen F, Ultsch A, Nöcker M, Stamm C (2005) Databionic visualization of music collections according to perceptual distance. In: Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK, pp 396–403Google Scholar
  7. Ras ZW, Wieczorkowska A (eds) (2010) Advances in music information retrieval. Springer, BerlinGoogle Scholar
  8. Typke R, Wiering F, Veltkamp RC (2005) A survey of music information retrieval systems. In: Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK, pp 153–160Google Scholar
  9. Vatolkin I, Theimer W, Botteck M (2010) AMUSE (Advanced MUSic Explorer) – A multitool framework for music data analysis. In: Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, Netherlands, pp 33–38Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claus Weihs
    • 1
  • Klaus Friedrichs
    • 1
  • Markus Eichhoff
    • 1
  • Igor Vatolkin
    • 2
  1. 1.Department of StatisticsTU Dortmund UniversityDortmundGermany
  2. 2.Department of Computer ScienceTU Dortmund UniversityDortmundGermany

Personalised recommendations