Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy
Sumit Kumar Banchhor and Arif Khan. Article: Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy. International Journal of Applied Information Systems 1(3):16-19, February 2012. BibTeX
@article{key:article, author = "Sumit Kumar Banchhor and Arif Khan", title = "Article: Musical Instrument Recognition using Zero Crossing Rate and Short-time Energy", journal = "International Journal of Applied Information Systems", year = 2012, volume = 1, number = 3, pages = "16-19", month = "February", note = "Published by Foundation of Computer Science, New York, USA" }
Abstract
Traditionally, musical instrument recognition is mainly based on frequency domain analysis (sinusoidal analysis, cepstral coefficients) and shape analysis to extract a set of various features. Instruments are usually classified using k-NN classifiers, HMM, Kohonen SOM and Neural Networks. Recognition of musical instruments in multi-instrumental, polyphonic music is a difficult challenge which is yet far from being solved. Successful instrument recognition techniques in solos (monophonic or polyphonic recordings of single instruments) can help to deal with this task. We introduce an instrument recognition process in solo recordings of a set of instruments (flute, guitar and harmonium), which yields a high recognition rate. A large solo database is used in order to encompass the different sound possibilities of each instrument and evaluate the generalization abilities of the classification process. The basic characteristics are computed in 1sec interval and result shows that the estimation of zero crossing rate and short time energy reflects more effectively the difference in musical instruments.
Reference
- K.D. Martin: Sound-Source Recognition: A Theory and Computational Model, Ph.D. thesis, MIT, 1999
- A. Livshin, X. Rodet: Musical Instrument Identification in Continuous Recordings, Proc. of the 7th Int. Conference on Digital Audio Effects (DAFX-04), Naples, Italy, October 5-8, 2004
- A. Eronen, A. Klapuri: Musical Instrument Recognition Using Cepstral Coefficients and Temporal Features, Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2000, pp. 753-756
- T. Kitahara, M. Goto, H. Okuno: Musical Instrument Identification Based on F0-Dependent Multivariate Normal Distribution, Proc. of the 2003 IEEE Int'l Conf. on Acoustic, Speech and Signal Processing (ICASSP '03), Vol.V, pp.421-424, Apr. 2003
- A. Eronen: Musical instrument recognition using ICA-based transform of features and discriminatively trained HMMs, Proc. of the Seventh International Symposium on Signal Processing and its Applications, ISSPA 2003, Paris, France, 1-4 July 2003, pp. 133-136
- G. De Poli, P. Prandoni: Sonological Models for Timbre Characterization, Journal of New Music Research, Vol 26 (1997), pp. 170-197, 1997
Keywords
Musical Instrument classification, generalization, zero crossing rate, short time energy