Dynamic Musical Orchestration Using Genetic Algorithms and a Spectro-Temporal Description of Musical Instruments

  • Philippe Esling
  • Grégoire Carpentier
  • Carlos Agon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)


In this paper a computational approach of musical orchestration is presented. We consider orchestration as the search of relevant sound combinations within large instruments sample databases. The working environment is Orchidée an evolutionary orchestration algorithm that allows a constrained multiobjective search towards a target timbre, in which several perceptual dimensions are jointly optimized. Up until now, Orchidée was bounded to “time-blind” features, by the use of averaged descriptors over the whole spectrum. We introduce a new instrumental model based on Gaussian Mixture Models (GMM) which allows to represent the complete spectro-temporal structure. We then present the results of the integration of our model and improvement that it brings to the existing system.


Orchestration Genetic Algorithms Gaussian Mixture Models Instruments Temporal Evolution Instrumental Models 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, J.C., Puckette, M.S.: An Efficient Algorithm for the Calculation of a Constant Q Transform. Journal of the Acoustic Society of America 92(5), 2698–2701 (1992)CrossRefGoogle Scholar
  2. 2.
    Carpentier, G., Assayag, G., Saint-James, E.: Solving the Musical Orchestration Problem using Multiobjective Constrained Optimization with a Genetic Local Search Approach. Heuristics (2009) (in Print)Google Scholar
  3. 3.
    Carpentier, G., Bresson, J.: Interacting with Symbolic, Sound and Feature Spaces in Orchidée, a Computer-Aided Orchestration Environment. Computer Music Journal 34(1) (2010)Google Scholar
  4. 4.
    Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)CrossRefGoogle Scholar
  5. 5.
    Jensen, K.: Musical Instruments Parametric Evolution. In: Proceedings of the ISMA, Mexico City, Mexico (2002)Google Scholar
  6. 6.
    Kameoka, H., Nishimoto, T., Sagayama, S.: A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering. IEEE Transactions on Audio, Speech and language processing 15(3) (2007)Google Scholar
  7. 7.
    McAdams, S., Beauchamp, J.W., Meneguzzi, S.: Discrimination of Musical Instrument Sounds Resynthesized with Simplified Spectrotemporal Parameters. Journal of the Acoustical Society of America 105(2), 882–897 (1999)CrossRefGoogle Scholar
  8. 8.
    Nouno, G., Cont, A., Carpentier, G., Harvey, J.: Making an Orchestra Speak. In: Proceedings of the Sound and Music Computing Conference, Porto, Portugal, pp. 277–282 (2009)Google Scholar
  9. 9.
    Peeters, G.: Automatic Classification of Large Musical Instrument Databases using Hierachical Classifiers with Inertia Ratio Maximization. In: Proceedings of the 115th Audio Engineering Society Convention, New York, USA (2003)Google Scholar
  10. 10.
    Sukittanon, S., Atlas, L.E., Pitton, J.W.: Modulation-Scale Analysis for Content Identification. IEEE Transactions on Signal Processing 52(10), 3023–3035 (2004)CrossRefGoogle Scholar
  11. 11.
    Tardieu, D., Peeters, G., Rodet, X.: An Instrument Timbre Model for Computer Aided Orchestration. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, New York (2007)Google Scholar
  12. 12.
    Verbeek, J.J., Vlassis, N., Kröse, B.: Efficient Greedy Learning of Gaussian Mixture Model. Neural Computation 5(2), 469–485 (2003)CrossRefGoogle Scholar
  13. 13.
    Virtanen, T., Klapuri, A.: Analysis of Polyphonic Audio Using Source-Filter Model and Non-Negative Matrix Factorization. In: Advances in Models for Acoustic Processing, Neural Information Processing Systems Workshop (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Philippe Esling
    • 1
  • Grégoire Carpentier
    • 1
  • Carlos Agon
    • 1
  1. 1.Institut de Recherche et Coordination Acoustique / MusiqueParisFrance

Personalised recommendations