Abstract
In our research we deal with polyphonic audio data, containing layered sounds of representing various timbres. Real audio recordings, musical instrument sounds of definite pitch, and artificial sounds of definite and indefinite pitch were applied in this research. Our experiments included preparing training and testing data, as well as classification of these data. In this paper we describe how results obtained from classification allowed us to discover abnormalities in the data, then adjust the data accordingly, and improve the classification results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aniola, P., Lukasik, E.: JAVA Library for Automatic Musical Instruments Recognition. In: AES 122 Convention, Vienna, Austria (2007)
Brown, J.C.: Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. J. Acoust. Soc. Am. 105, 1933–1941 (1999)
Herrera, P., Amatriain, X., Batlle, E., Serra, X.: Towards instrument segmentation for music content description: a critical review of instrument classification techniques. In: International Symposium on Music Information Retrieval ISMIR (2000)
Kaminskyj, I.: Multi-feature Musical Instrument Sound Classifier w/user determined generalisation performance. In: Australasian Computer Music Association Conference ACMC, pp. 53–62 (2002)
Kitahara, T., Goto, M., Okuno, H.G.: Pitch-Dependent Identification of Musical Instrument Sounds. Applied Intelligence 23, 267–275 (2005)
Klapuri, A., Virtanen, T., Eronen, A., Seppanen, J.: Automatic transcription of musical recordings. In: Consistent and Reliable Acoustic Cues for sound analysis CRAC Workshop (2001)
Kostek, B., Dziubinski, M., Dalka, P.: Estimation of Musical Sound Separation Algorithm Efectiveness Employing Neural Networks. Journal of Intelligent Information Systems 24, 133–157 (2005)
Livshin, A., Rodet, X.: The importance of cross database evaluation in musical instrument sound classification: A critical approach. In: International Symposium on Music Information Retrieval ISMIR (2003)
Martin, K.D., Kim, Y.E.: Musical instrument identification: A pattern-recognition approach. In: 136th meeting of the Acoustical Society of America, Norfolk, VA (1998)
ISO/IEC JTC1/SC29/WG11: MPEG-7 Overview, http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm
Opolko, F., Wapnick, J.: MUMS - McGill University Master Samples. CD’s (1987)
Peeters, G., McAdams, S., Herrera, P.: Instrument Sound Description in the Context of MPEG-7. In: International Computer Music Conference ICMC 2000 (2000)
The University of Waikato: Weka Machine Learning Project, http://www.cs.waikato.ac.nz/~ml/
Virtanen, T.: Algorithm for the separation of harmonic sounds with time-frequency smoothness constraint. In: 6th International Conference on Digital Audio Effects DAFX (2003)
Viste, H., Evangelista, G.: Separation of Harmonic Instruments with Overlapping Partials in Multi-Channel Mixtures. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics WASPAA 2003 (2003)
Wieczorkowska, A.: Wavelet Based Analysis and Parameterization of Musical Instrument Sounds. In: ISSEM 1999, pp. 219–224 (1999)
Wieczorkowska, A., Kolczyńska, E.: Quality of Musical Instrument Sound Identification for Various Levels of Accompanying Sounds. In: Raś, Z.W., Tsumoto, S., Zighed, D.A. (eds.) MCD 2007. LNCS (LNAI), vol. 4944, pp. 93–103. Springer, Heidelberg (2008)
Wieczorkowska, A., Kolczyńska, E.: Identification of Dominating Instrument in Mixes of Sounds of the Same Pitch. In: Ann, A., Matwin, S., Raś, Ś.D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 455–464. Springer, Heidelberg (2008)
Zhang, X., Ras, Z.: Discriminant feature analysis for music timbre recognition. In: ECML/PKDD Third International Workshop on Mining Complex Data (MCD 2007), pp. 59–70 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wieczorkowska, A. (2008). Learning from Soft-Computing Methods on Abnormalities in Audio Data . In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-88425-5_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88423-1
Online ISBN: 978-3-540-88425-5
eBook Packages: Computer ScienceComputer Science (R0)