Training of Classifiers for the Recognition of Musical Instrument Dominating in the Same-Pitch Mix
Preparing a database to train classifiers for identification of musical instruments in audio files is very important, especially in a case of sounds of the same pitch, when a dominating instrument is most difficult to identify. Since it is infeasible to prepare a data set representing all possible ever recorded mixes, we had to reduce the number of sounds in our research to a reasonable size. In this paper, our data set represents sounds of selected instruments of the same octave, with additions of artificial sounds of broadband spectra for training, and additions of sounds of other instruments for testing purposes. We tested various levels of added sounds taking into consideration only equal steps in logarithmic scale which are more suitable for amplitude comparison than linear one. Additionally, since musical instruments can be classified hierarchically, experiments for groups of instruments representing particular nodes of such hierarchy have been also performed. The set-up of training and testing sets, as well as experiments on classification of the instrument dominating in the sound file, are presented and discussed in this paper.
KeywordsMusical Instrument Music Information Retrieval Automatic Indexing Broadband Spectrum Adobe Audition
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- 1.Adobe Systems Incorporated: Adobe Audition 1.0 (2003)Google Scholar
- 2.Aniola, P., Lukasik, E.: JAVA Library for Automatic Musical Instruments Recognition. AES 122 Convention, Vienna, Austria (May 2007)Google Scholar
- 4.Herrera, P., Amatriain, X., Batlle, E., Serra, X.: Towards instrument segmentation for music content description: a critical review of instrument classification techniques. In: Int. Symp. on Music Information Retrieval ISMIR 2000, Plymouth, MA (2000)Google Scholar
- 5.Hornbostel, E.M.V., Sachs, C.: Systematik der Musikinstrumente. Ein Versuch. Zeitschrift fur Ethnologie 46(4-5), 553–590 (1914)Google Scholar
- 6.ISO/IEC JTC1/SC29/WG11: MPEG-7 Overview (2004), http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm
- 7.Kaminskyj, I.: Multi-feature Musical Instrument Sound Classifier w/user determined generalisation performance. In: Proceedings of the Australasian Computer Music Association Conference ACMC, pp. 53–62 (2002)Google Scholar
- 8.Martin, K.D., Kim, Y.E.: Musical instrument identification: A pattern-recognition approach. 136-th meeting of the Acoustical Society of America, Norfolk, VA (1998)Google Scholar
- 9.Opolko, F., Wapnick, J.: MUMS - McGill University Master Samples. CD’s (1987)Google Scholar
- 10.Ras, Z.W., Zhang, X., Lewis, R.: MIRAI: Multi-hierarchical, FS-Tree Based Music Information Retrieval System. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 80–89. Springer, Heidelberg (2007)Google Scholar
- 11.The University of Waikato: Weka Machine Learning Project. Internet (2007), http://www.cs.waikato.ac.nz/~ml/
- 13.Wieczorkowska, A., Kolczyńska, E.: Quality of Musical Instrument Sound Identification for Various Levels of Accompanying Sounds. In: Ras, Z.W., Tsumoto, S., Zighead, D. (eds.) Mining Complex Data, Post-proceedings. LNCS/LNAI (2007)Google Scholar
- 14.Zhang, X.: Cooperative Music Retrieval Based on Automatic Indexing of Music by Instruments and Their Types. Ph.D thesis, Univ. North Carolina, Charlotte (2007)Google Scholar