Automatic Extraction of Approximate Repetitions in Polyphonic Midi Files Based on Perceptive Criteria

  • Benoit Meudic
  • Emmanuel St-James
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2771)


In the context of musical analysis, we propose an algorithm that automatically induces patterns from polyphonies. We define patterns as “perceptible repetitions in a musical piece”. The algorithm that measures the repetitions relies on some general perceptive notions: it is non-linear, non-symetric and non-transitive. The model can analyse any music of any genre that contains a beat. The analysis is performed into three stages. First, we quantize a MIDI sequence and we segment the music in “beat segments”. Then, we compute a similarity matrix from the segmented sequence. The measure of similarity relies on features such as rhythm, contour and pitch intervals. Last, a bottom-up approach is proposed for extracting patterns from the similarity matrix. The algorithm was tested on several pieces of music, and some examples will be presented in this paper.


Similarity Matrix Similarity Matrice Automatic Extraction Pitch Contour Musical Piece 
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.


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  1. 1.
    Peteers, G., et al.: Toward Automatic Music Audio Summary Generation from Signal Analysis. Proc. Ismir, Paris (2002) Google Scholar
  2. 2.
    Lartillot, O.: Perception-Based Advanced Description of Abstract Musical Content. In: Proc. WIAMIS, London (2003)Google Scholar
  3. 3.
    Rolland, P.-Y.: Discovering patterns in musical sequences. JNMR 28(4), 334–350 (1999)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Meredith, et al.: Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Cambridge Music Processing Colloquium (2003)Google Scholar
  5. 5.
    Cambouropoulos, E.: Owards a General Computational Theory of Musical Structure, PhD, Edinburgh, Faculty of Music and Department of Artificial Intelligence (1998)Google Scholar
  6. 6.
    Meudic, B.: A causal algorithm for beat tracking. In: 2nd conference on understanding and creating music, Caserta, Italy (2002)Google Scholar
  7. 7.
    Tversky, A.: Features of similarity. ournal of Psychological Review, 327–352 (1977)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Benoit Meudic
    • 1
  • Emmanuel St-James
    • 2
  1. 1.Musical representation team – IRCAMParisFrance
  2. 2.LIP6/SRCUniversité Pierre et Marie CurieParisFrance

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