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Songs2See and GlobalMusic2One: Two Applied Research Projects in Music Information Retrieval at Fraunhofer IDMT

  • Christian Dittmar
  • Holger Großmann
  • Estefanía Cano
  • Sascha Grollmisch
  • Hanna Lukashevich
  • Jakob Abeßer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6684)

Abstract

At the Fraunhofer Institute for Digital Media Technology (IDMT) in Ilmenau, Germany, two current research projects are directed towards core problems of Music Information Retrieval. The Songs2See project is supported by the Thuringian Ministry of Economy, Employment and Technology through granting funds of the European Fund for Regional Development. The target outcome of this project is a web-based application that assists music students with their instrumental exercises. The unique advantage over existing e-learning solutions is the opportunity to create personalized exercise content using the favorite songs of the music student. GlobalMusic2one is a research project supported by the German Ministry of Education and Research. It is set out to develop a new generation of hybrid music search and recommendation engines. The target outcomes are novel adaptive methods of Music Information Retrieval in combination with Web 2.0 technologies for better quality in the automated recommendation and online marketing of world music collections.

Keywords

music information retrieval automatic music transcription music source separation automatic music annotation music similarity search music education games 

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References

  1. 1.
    Abeßer, J., Dittmar, C., Großmann, H.: Automatic genre and artist classification by analyzing improvised solo parts from musical recordings. In: Proceedings of the Audio Mostly Conference (AMC), Piteå, Sweden (2008)Google Scholar
  2. 2.
    Abeßer, J., Bräuer, P., Lukashevich, H., Schuller, G.: Bass playing style detection based on high-level features and pattern similarity. In: Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), Utrecht, Netherlands (2010)Google Scholar
  3. 3.
    Abeßer, J., Lukashevich, H., Dittmar, C., Bräuer, P., Krause, F.: Rule-based classification of musical genres from a global cultural background. In: Proceedings of the 7th International Symposium on Computer Music Modeling and Retrieval (CMMR), Malaga, Spain (2010)Google Scholar
  4. 4.
    Abeßer, J., Lukashevich, H., Dittmar, C., Schuller, G.: Genre classification using bass-related high-level features and playing styles. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan (2009)Google Scholar
  5. 5.
    Abeßer, J., Lukashevich, H., Schuller, G.: Feature-based extraction of plucking and expression styles of the electric bass guitar. In: Proceedings of the IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Dallas, Texas, USA (2010)Google Scholar
  6. 6.
    Arndt, D., Gatzsche, G., Mehnert, M.: Symmetry model based key finding. In: Proceedings of the 126th AES Convention, Munich, Germany (2009)Google Scholar
  7. 7.
    Barbancho, A., Barbancho, I., Tardon, L., Urdiales, C.: Automatic edition of songs for guitar hero/frets on fire. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), New York, USA (2009)Google Scholar
  8. 8.
    Bertin-Mahieux, T., Eck, D., Maillet, F., Lamere, P.: Autotagger: a model for predicting social tags from acoustic features on large music databases. Journal of New Music Research 37(2), 115–135 (2008)CrossRefGoogle Scholar
  9. 9.
    Cano, E., Cheng, C.: Melody line detection and source separation in classical saxophone recordings. In: Proceedings of the 12th International Conference on Digital Audio Effects (DAFx), Como, Italy (2009)Google Scholar
  10. 10.
    Cano, E., Schuller, G., Dittmar, C.: Exploring phase information in sound source separation applications. In: Proceedings of the 13th International Conference on Digital Audio Effects (DAFx 2010), Graz, Austria (2010)Google Scholar
  11. 11.
    Dittmar, C., Dressler, K., Rosenbauer, K.: A toolbox for automatic transcription of polyphonic music. In: Proceedings of the Audio Mostly Conference (AMC), Ilmenau, Germany (2007)Google Scholar
  12. 12.
    Dittmar, C., Großmann, H., Cano, E., Grollmisch, S., Lukashevich, H., Abeßer, J.: Songs2See and GlobalMusic2One - Two ongoing projects in Music Information Retrieval at Fraunhofer IDMT. In: Proceedings of the 7th International Symposium on Computer Music Modeling and Retrieval (CMMR), Malaga, Spain (2010)Google Scholar
  13. 13.
    Duan, Z., Pardo, B., Zhang, C.: Multiple fundamental frequency estimation by modeling spectral peaks and non-peak regions. EEE Transactions on Audio, Speech, and Language Processing (99), 1–1 (2010)Google Scholar
  14. 14.
    Fitzgerald, D., Cranitch, M., Coyle, E.: Extended nonnegative tensor factorization models for musical sound source separation. Computational Intelligence and Neuroscience (2008)Google Scholar
  15. 15.
    Gärtner, D.: Singing / rap classification of isolated vocal tracks. In: Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), Utrecht, Netherlands (2010)Google Scholar
  16. 16.
    Gómez, E., Haro, M., Herrera, P.: Music and geography: Content description of musical audio from different parts of the world. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan (2009)Google Scholar
  17. 17.
    Grollmisch, S., Dittmar, C., Cano, E.: Songs2see: Learn to play by playing. In: Proceedings of the 41st AES International Conference on Audio in Games, London, UK (2011)Google Scholar
  18. 18.
    Grollmisch, S., Dittmar, C., Cano, E., Dressler, K.: Server based pitch detection for web applications. In: Proceedings of the 41st AES International Conference on Audio in Games, London, UK (2011)Google Scholar
  19. 19.
    Grollmisch, S., Dittmar, C., Gatzsche, G.: Implementation and evaluation of an improvisation based music video game. In: Proceedings of the IEEE Consumer Electronics Society’s Games Innovation Conference (IEEE GIC), London, UK (2009)Google Scholar
  20. 20.
    Gruhne, M., Schmidt, K., Dittmar, C.: Phoneme recognition on popular music. In: 8th International Conference on Music Information Retrieval (ISMIR), Vienna, Austria (2007)Google Scholar
  21. 21.
    Herrera, P., Sandvold, V., Gouyon, F.: Percussion-related semantic descriptors of music audio files. In: Proceedings of the 25th International AES Conference, London, UK (2004)Google Scholar
  22. 22.
    Kahl, M., Abeßer, J., Dittmar, C., Großmann, H.: Automatic recognition of tonal instruments in polyphonic music from different cultural backgrounds. In: Proceedings of the 36th Jahrestagung für Akustik (DAGA), Berlin, Germany (2010)Google Scholar
  23. 23.
    Klapuri, A.: A method for visualizing the pitch content of polyphonic music signals. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan (2009)Google Scholar
  24. 24.
    Klapuri, A., Davy, M. (eds.): Signal Processing Methods for Music Transcription. Springer Science + Business Media LLC, New York (2006)Google Scholar
  25. 25.
    Lidy, T., Rauber, A., Pertusa, A., Iesta, J.M.: Improving genre classification by combination of audio and symbolic descriptors using a transcription system. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR), Vienna, Austria (2007)Google Scholar
  26. 26.
    Lukashevich, H.: Towards quantitative measures of evaluating song segmentation. In: Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR), Philadelphia, Pennsylvania, USA (2008)Google Scholar
  27. 27.
    Lukashevich, H.: Applying multiple kernel learning to automatic genre classification. In: Proceedings of the 34th Annual Conference of the German Classification Society (GfKl), Karlsruhe, Germany (2010)Google Scholar
  28. 28.
    Lukashevich, H., Abeßer, J., Dittmar, C., Großmann, H.: From multi-labeling to multi-domain-labeling: A novel two-dimensional approach to music genre classification. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan (2009)Google Scholar
  29. 29.
    Mercado, P., Lukashevich, H.: Applying constrained clustering for active exploration of music collections. In: Proceedings of the 1st Workshop on Music Recommendation and Discovery (WOMRAD), Barcelona, Spain (2010)Google Scholar
  30. 30.
    Mercado, P., Lukashevich, H.: Feature selection in clustering with constraints: Application to active exploration of music collections. In: Proceedings of the 9th Int. Conference on Machine Learning and Applications (ICMLA), Washington DC, USA (2010)Google Scholar
  31. 31.
    Ono, N., Miyamoto, K., Roux, J.L., Kameoka, H., Sagayama, S.: Separation of a monaural audio signal into harmonic/percussive components by complememntary diffusion on spectrogram. In: Proceedings of the 16th European Signal Processing Conferenc (EUSIPCO), Lausanne, Switzerland (2008)Google Scholar
  32. 32.
    Pohle, T., Schnitzer, D., Schedl, M., Knees, P., Widmer, G.: On rhythm and general music similarity. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan (2009)Google Scholar
  33. 33.
    Ryynänen, M., Klapuri, A.: Automatic transcription of melody, bass line, and chords in polyphonic music. Computer Music Journal 32, 72–86 (2008)CrossRefGoogle Scholar
  34. 34.
    Sagayama, S., Takahashi, K., Kameoka, H., Nishimoto, T.: Specmurt anasylis: A piano-roll-visualization of polyphonic music signal by deconvolution of log-frequency spectrum. In: Proceedings of the ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing (SAPA), Jeju, Korea (2004)Google Scholar
  35. 35.
    Shashanka, M., Raj, B., Smaragdis, P.: Probabilistic latent variable models as nonnegative factorizations. Computational Intelligence and Neuroscience (2008)Google Scholar
  36. 36.
    Smaragdis, P., Mysore, G.J.: Separation by “humming”: User-guided sound extraction from monophonic mixtures. In: Proceedings of IEEE Workshop on Applications Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA (2009)Google Scholar
  37. 37.
    Stein, M., Schubert, B.M., Gruhne, M., Gatzsche, G., Mehnert, M.: Evaluation and comparison of audio chroma feature extraction methods. In: Proceedings of the 126th AES Convention, Munich, Germany (2009)Google Scholar
  38. 38.
    Stober, S., Nürnberger, A.: Towards user-adaptive structuring and organization of music collections. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds.) AMR 2008. LNCS, vol. 5811, pp. 53–65. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  39. 39.
    Tzanetakis, G., Kapur, A., Schloss, W.A., Wright, M.: Computational ethnomusicology. Journal of Interdisciplinary Music Studies 1(2), 1–24 (2007)Google Scholar
  40. 40.
    Uhle, C.: Automatisierte Extraktion rhythmischer Merkmale zur Anwendung in Music Information Retrieval-Systemen. Ph.D. thesis, Ilmenau University, Ilmenau, Germany (2008)Google Scholar
  41. 41.
    Vinyes, M., Bonada, J., Loscos, A.: Demixing commercial music productions via human-assisted time-frequency masking. In: Proceedings of the 120th AES convenction, Paris, France (2006), http://www.mtg.upf.edu/files/publications/271dd4-AES120-mvinyes-jbonada-aloscos.pdf (last viewed February 2011)
  42. 42.
    Völkel, T., Abeßer, J., Dittmar, C., Großmann, H.: Automatic genre classification of latin american music using characteristic rhythmic patterns. In: Proceedings of the Audio Mostly Conference (AMC), Piteå, Sweden (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Dittmar
    • 1
  • Holger Großmann
    • 1
  • Estefanía Cano
    • 1
  • Sascha Grollmisch
    • 1
  • Hanna Lukashevich
    • 1
  • Jakob Abeßer
    • 1
  1. 1.Fraunhofer IDMTIlmenauGermany

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