Rough set based automatic classification of musical instrument sounds

Electronic Notes in Theoretical Computer Science

Volumn 82, Issue 4, 2003, Pages 298-309

  Wieczorkowska, Alicja A ^a   Czyzewski, Andrzej ^b  

^a POLISH JAPANESE INSTITUTE OF INFORMATION TECHNOLOGY   (Poland)

^b GDANSK UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Audio data classification; Rough sets; Sound processing

Indexed keywords

AUDIO ACOUSTICS; AUTOMATION; DATA MINING; DATA REDUCTION; ELECTRONIC MUSICAL INSTRUMENTS; FOURIER TRANSFORMS; FREQUENCIES;

AUDIO DATA CLASSIFICATION; MUSICAL RANGES; PARAMETERIZATION; SOUND PROCESSING;

COMPUTER MUSIC;

EID: 18944380210     PISSN: 15710661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1571-0661(04)80727-8     Document Type: Conference Paper

References (20)

1. American National Standards Institute, "USA Standard Acoustical Terminology (Including Mechanical Shock and Vibration) S1.1-1960 (R1976)," New York: American National Standards Institute, 1960.
2. Brown J.C. "Computer identification of musical instruments using pattern recognition with cepstral coefficients as features," J. Acoust. Soc. of America. 105:1999;1933-1941
3. Brown J.C., Houix O., McAdams S. "Feature dependence in the automatic identification of musical woodwind instruments," J. Acoust. Soc. of America. 109:2001;1064-1072
4. Fujinaga I. and K. McMillan, "Realtime recognition of orchestral instruments," Proceedings of the International Computer Music Conference (2000), 141-143.
5. Herrera P., X. Amatriain, E. Batlle, and X. Serra, "Towards instrument segmentation for music content description: a critical review of instrument classification techniques," Proc. of International Symposium on Music Information Retrieval (ISMIR 2000), Plymouth, MA, 2000.
6. ISO/IEC JTC1/SC29/WG11, "MPEG-7 Overview" (2002), URL: http://mpeg.telecomitalialab.com/standards/mpeg-7/mpeg-7.htm
7. Kaminskyj I. "Multi-feature Musical Instrument Classifier," Mikro Polyphonie. 6:2000;. online contemporary music journal, http://farben.latrobe.edu.au/.
8. Keele Jr., D. B., "Time-frequency display of electro-acoustic data using cycleoctave wavelet transforms," 99th AES Conv., New York (1995), preprint 4136.
9. Kostek B., Czyzewski A. "Representing Musical Instrument Sounds for Their Automatic Classification" J. Audio Eng. Soc. 49:(9):2001;768-785
10. Kostek, B. and A. Wieczorkowska, "Parametric Representation of Musical Sounds," Archive of Acoustics 22(1) (1997), Institute of Fundamental Technological Research, Warsaw, Poland, 3-26.
11. Kronland-Martinet R., Grossmann A. "Application of time-frequency and time-scale methods (wavelet transforms) to the analysis, synthesis, and transformation of natural sounds" De Poli G., Piccialli A., Roads C. Representations of Musical Signals. 1991;45-85 MIT Press
12. Lubniewski Z., Stepnowski A. "Sea bottom recognition method using fractal analysis and scattering impulse response" Archives of Acoustics. 23:1998;499-511
13. Martin, K. D. and Y. E. Kim, "2pMU9. Musical instrument identification: A pattern-recognition approach," 136-th meeting of the Acoustical Soc. of America, Norfolk, VA., 1998.
14. Opolko F. and J. Wapnick, "MUMS - McGill University Master Samples," CD's, 1987.
15. Pollard H.F., Jansson E.V. "A Tristimulus Method for the Specification of Musical Timbre," Acustica. 51:1982;162-171
16. Reduct Systems, "DataLogic/R," Regina, Saskatchewan, Canada 1990-95.
17. Wieczorkowska A., "Wavelet based analysis and parameterization of musical instrument sounds," in Proceedings of the International Symposium on Sound Engineering and Mastering ISSEM'99, Technical University of Gdansk, Poland (1999), 219-224.
18. Wieczorkowska A., "The recognition efficiency of musical instrument sounds depending on parameterization and type of a classifier," Ph.D. thesis, Technical University of Gdansk, Poland, 1999.
19. Wieczorkowska A. "Musical Sound Classification based on Wavelet Analysis," Fundamenta Informaticae. 47:(1/2):2001;175-188
20. Wolfram Research, "Mathematica, Wavelet Explorer," Champaign, Illinois, 1996.