Investigative Studies on Timbre of Musical Instruments using Spectral Analysis and Artificial Neural Network Techniques

International Journal of Applied Physics

© 2017 by SSRG - IJAP Journal

Volume 4 Issue 2

Year of Publication : 2017

Authors : Y. G. Parimala, Dr. B. Munibhadrayya, Dr. Suma Sudhindra

10.14445/23500301/IJAP-V4I4P104

How to Cite?

Y. G. Parimala, Dr. B. Munibhadrayya, Dr. Suma Sudhindra, "Investigative Studies on Timbre of Musical Instruments using Spectral Analysis and Artificial Neural Network Techniques," SSRG International Journal of Applied Physics, vol. 4,  no. 2, pp. 20-29, 2017. Crossref, https://doi.org/10.14445/23500301/IJAP-V4I4P104

Abstract:

Spectral content and frequency analysis were carried out on audio signals from various musical instruments using audio signal processing tools and Artificial Neural Network techniques to study the timbre of musical instruments. The nature of peaks produced and their amplitudes for audio inputs of a reference pitch and few melodies were investigated. Significant differences in the frequency spectra were observed which are characteristic of the type of instruments such as string, wind or percussion and rhythmic instruments which provides a means for identification of the category of an instrument. Artificial neural network approach is found to be extremely useful in identification of timbre or tone color of instruments and in identification of instruments. 100% accurate results were obtained on the preliminary investigations.

Keywords:

Timbre, Spectral content, neural network, pitch

References:

[1] P Stoica & R Moses. "Spectral Analysis of Signals" , 2005 An Introduction to the Theory of Random Signals and Noise
[2] Wilbur B. Davenport and William L. Root, IEEE Press, New York,, ISBN 0-87942-235-1, 1987
[3] Dixon Ward, W. "Musical Perception". In Foundations of Modern Auditory Theory vol. 1, edited by Jerry V. Tobias,]. New York: Academic Press. ISBN 0-12-691901-1, 1970
[4] Sethares, William . Tuning, Timbre, Spectrum, Scale. Berlin, London, and New York: Springer. ISBN 3-540-76173-X, 1998
[5] Wessel, David . "Low Dimensional Control of Musical Timbre". Computer Music Journal 3:45–52. Rewritten version, 1999, as "Timbre Space as a Musical Control Structure", 1979
[6] Schouten, J. F. "The Perception of Timbre". In Reports of the 6th International Congress on Acoustics, Tokyo, GP-6-2, 6 vols., edited by Y. Kohasi, 35–44, 90. Tokyo: Maruzen; Amsterdam: Elsevier, 1968
[7] Eronen, Antti, and Anssi Klapuri. "Musical instrument recognition using cepstral coefficients and temporal features." Acoustics, Speech, and Signal Processing, 2000. ICASSP'00. Proceedings. 2000 IEEE International Conference on. Vol. 2. IEEE, 2000.
[8] Peters, Geoffroy. "Music pitch representation by periodicity measures based on combined temporal and spectral representations." Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on. Vol. 5. IEEE, 2006.
[9] Kaminskyj and T. Czaszejko, “Automatic recognition of isolated monophonic musical instrument sounds using knnc,” Journal of Intelligent Information Systems, vol. 24, no. 2/3, pp. 199–221, 2005
[10] .Essid, Slim, Gaël Richard, and Bertrand David. "Instrument recognition in polyphonic music." Acoustics, Speech, and Signal Processing, 2005. Proceedings.(ICASSP'05). IEEE International Conference on. Vol. 3. IEEE, 2005.
[11] A.A. Livshin and X. Rodet, “Musical instrument identification in continuous recordings,” in Proceedings of the 7th International Conference on Digital Audio Effects, pp. 222–226, 2004
[12] Satish Ramling Sankaye et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Perspective Identification of Indian Musical Instrument Ghan and Sushir Vadya, Vol. 6 (3) , 2880-2883, 2005
[13] Asbjørn Krokstad, Nonuniformity in timbre of string instruments, The Journal of the Acoustical Society of America 82, S69 ; doi: 10.1121/1.2024936, 1987
[14] SIR C V RAMAN, Kt., F.R.S., N.L. Proc. Indian Acad. Sci. A1 179-188 , The Indian musical drums, 1935.
[15] Judd, J.S. Neural Network Design and the Complexity of Learning, MA:The MIT Press. 1990