Dynamic Neural Networks Applied to Melody Retrieval

  • Laura E. Gomez
  • Humberto Sossa
  • Ricardo Barron
  • Julio F. Jimenez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)


A new method for the retrieval of melodies from a database is described in this paper. For its functioning, the method makes use of Dynamic Neural Networks (DNN). During training a set ofDNN is first trained with information of the melodies to be retrieved. Instead of using traditional signal descriptors we use the matrix of synaptic weights that can be efficiently used for melody representation and retrieval. Most of the reported works have been focused on the symbolic representation of musical information. None of them have provided good results with original signals.


Music Information Retrieval Dynamic Neuronal Networks Musical Descriptors 


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  1. 1.
    Ghias, A.: Query By Humming-Musical Information Retrieval in an Audio Database. Proc.s of ACM Multimedia 95, 231–236 (1995)Google Scholar
  2. 2.
    Blackburn, S., De Roure, D.: A Tool for Content Based Navigation of Music. Proc. ACM Multimedia 98, 361–368 (1998)Google Scholar
  3. 3.
    McNab, R.J.: Towards the Digital Music Library: Tune Retrieval from Acoustic Input. In: Proc. of Digital Libraries, pp. 11–18 (1996)Google Scholar
  4. 4.
    Lemstrom, K., Laine, P., Perttu, S.: Using Relative Interval Slope in Music Information. Retrieval. In: Proc. of International Computer Music Conference 1999 (ICMC 1999), pp. 317–320 (1999)Google Scholar
  5. 5.
    Chen, A.L.P., Chang, M., Chen, J.: Query by Music Segments: An Efficient Approach for Song Retrieval. In: Proc. of IEEE International Conference on Multimedia and Expo. (2000)Google Scholar
  6. 6.
    Francu, C., Nevill-Manning, C.G.: Distance Metrics and Indexing Strategies for a Digital Library of Popular Music. In: Proc. of IEEE International Conference on Multimedia and Expo. (2000)Google Scholar
  7. 7.
    Acosta, M., Salazar, H., Zuluaga, C.: Tutorial de Redes Neuronales, Universidad Tecnológica de Pereira (2000)Google Scholar
  8. 8.
    Kornstadt, A.: Themefinder: A web-based melodic search tool. In: Computing in Musicology, vol. 11, MIT Press, Cambridge (1998)Google Scholar
  9. 9.
    McNab, R.J., et al.: The New Zealand digital library melody index. Digital Libraries Magazine (1997)Google Scholar
  10. 10.
    Uitdenbogerd, A., Zobel, J.: Melodic matching techniques for large music databases. In: Proceedings of ACM Multimedia Conference, pp. 57–66 (1999)Google Scholar
  11. 11.
    Hwang, E., Rho, S.: FMF(fast melody finder): A web-based music retrieval system. In: Wiil, U.K. (ed.) CMMR 2003. LNCS, vol. 2771, pp. 179–192. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Hwang, E., Rho, S.: FMF: Query adaptive melody retrieval system. Journal of Systems and Software 79(1), 43–56 (2006)CrossRefGoogle Scholar
  13. 13.
    Zhuge, H.: An inexact model matching approach and its applications. Journal of Systems and Software 67(3), 201–212 (2003)CrossRefGoogle Scholar
  14. 14.
    Zhuge, H.: A problem-oriented and rule-based component repository. Journal of Systems and Software 50(3), 201–208 (2000)CrossRefGoogle Scholar
  15. 15.
    Pickens, J.: A comparison of language modeling and probabilistic text information retrieval approaches to monophonic music retrieval. In: Proceedings of the 1st Annual International Symposium on Music Information Retrieval, ISMIR 2000 (2000)Google Scholar
  16. 16.
    Lemstrom, K., Wiggins, G.A., Meredith, D.: A threelayer approach for music retrieval in large databases. In: Second International Symposium on Music Information Retrieval, Bloomington, IN, USA, pp. 13–14 (2001)Google Scholar
  17. 17.
    Hoashi, K., Matsumoto, K., Inoue, N.: Personalization of user profiles for content-based music retrieval based on relevance feedback. ACM Multimedia, 110–119 (2003)Google Scholar
  18. 18.
    Gerhard, D.: Pitch Extraction and Fundamental Frequency: History and Current Techniques. Technical Report TR-CS 2003-06 (2003)Google Scholar
  19. 19.
    Huang, R., Hansen, J.H.L.: Advanced in unsupervised audio classification and segmentation for the broadcast news and NGSW Corpora. IEEE Trans. on Audio Speech and Language Processing 14(3), 907–919 (2006)CrossRefGoogle Scholar
  20. 20.
    Forberg, J.: Automatic conversion of sound to the MIDIformat. TMH-QPSR 1-2/1998 (1998)Google Scholar
  21. 21.
    Ryynanen, M., Klapuri, A.: Transcription of the singing melody in polyphonic music, ISMIR 2006 (2006)Google Scholar
  22. 22.
    Klapuri, A.P.: A perceptually motivated multiple-f0 estimation method. In: 2005 IEEE workshop on applications of signal processing to audio and acoustics, pp. 291–294 (2005)Google Scholar
  23. 23.
    Typke, R., Prechelt, L.: An interface for melody input. ACM Transactions on Computer–Human Interaction, 133–149 (2001)Google Scholar
  24. 24.
    Ukkonen, E., Lemstrom, K., Makinen, V.: Sweepline the music. In: Klein, R., Six, H.-W., Wegner, L. (eds.) Computer Science in Perspective. LNCS, vol. 2598, pp. 330–342. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  25. 25.
    Suzuki, M., et al.: Music information retrieval from a singing voice based on verification of recognized hypothesis. In: ISMIR 2006 (2006)Google Scholar
  26. 26.
    McCann, J.A., et al.: Kendra: Adaptive Internet system. Journal of Systems and Software 55(1), 3–17 (2000)CrossRefGoogle Scholar
  27. 27.
    Huang, C.M., et al.: Synchronization and flow adaptation schemes for reliable multiple-stream transmission in multimedia presentation. Journal of Systems and Software 56(2), 133–151 (2001)CrossRefGoogle Scholar
  28. 28.
    Nelles, O.: Nonlinear system identification. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar
  29. 29.
    Yun, S.Y., Namkoong, S., Rho, J.H., Shin, S.W., Choi, J.U.: A performance evaluation of neural network models in traffic volume forecasting. Mathematic Computing Modelling 27(9-11), 293–310 (1998)CrossRefGoogle Scholar
  30. 30.
    Lingras, P., Mountford, P.: Time delay neural networks designed using genetic algorithms for short term inter-city traffic forecasting. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS (LNAI), vol. 2070, Springer, Heidelberg (2001)CrossRefGoogle Scholar
  31. 31.
    Campolucci, P., Uncini, A., Piazza, F., Rao, B.D.: On-line learning algorithms for locally recurrent neural networks. IEEE transactions on neural networks 10(2), 253–271 (1999)CrossRefGoogle Scholar
  32. 32.
    Tsai, T.-H., Lee, C.-K., Wei, C.-H.: Artificial Neural Networks Based Approach for Short-term Railway Passenger Demand Forecasting. Journal of the Eastern Asia Society for Transportation Studies 4, 221–235 (2003)Google Scholar
  33. 33.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. p. 837. Prentice Hall PTR, Englewood Cliffs (1998) ISBN 0-13-273350-1Google Scholar
  34. 34.
    Weibel, A., Hanazawa, T., Hinton, G., Shikano, K., Lang, K.: Phenomena Recognition Using Time-delay Neural Networks. IEEE Transactions on Acoustics, Speech, and Signal Processing 37, 328–339 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Laura E. Gomez
    • 1
  • Humberto Sossa
    • 1
  • Ricardo Barron
    • 1
  • Julio F. Jimenez
    • 1
  1. 1.Centro de Investigación en Computación-IPNUnidad Profesional Adolfo-López MateosZacatencoMexico

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