Abstract
In this paper, we propose a novel feature set for instrument classification which is based on the information rate of the signal in the time domain. The feature is extracted by calculating the Shannon entropy over a sliding short-time energy frame and binning statistical features into a unique feature vector. Experimental results are presented, including a comparison to frequency-domain feature sets. The proposed entropy features are shown to be faster than popular frequency-domain methods while maintaining comparable accuracy in an instrument classification task.
This is a preview of subscription content, log in via an institution.
References
Altaf, M., Juang, B.: Audio signal classification with temporal envelopes. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 469–472, May 2011
Delgado-Contreras, J., Garcia-Vazquez, J.: Classification of environmental audio signals using statistical time and frequency features. In: International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 212–216 (2014)
Deng, J., Simmermacher, C.: A study on feature analysis for musical instrument classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 38(2), 429–438 (2008)
Erdol, N., Castelluccia, C., Zilouchian, A.: Recovery of missing speech packets using the short-time energy and zero-crossing measurements. IEEE Trans. Speech Audio Process. 1(3), 295–303 (1993)
Eronen, A.: Comparison of features for musical instrument recognition. In: IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics, pp. 19–22 (2001)
Herrera-Boyer, P., Dubnov, S.: Automatic classification of musical instrument sounds. J. New Music Res. 32(1), 3–21 (2003)
Ibarrola, A., Chavez, E.: A robust entropy-based audio-fingerprint. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1729–1732, July 2006
Lambrou, T., Kudumakis, P., Speller, R., Sandler, M., Linney, A.: Classification of audio signals using statistical features on time and wavelet transform domains. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 6, pp. 3621–3624, May 1998
Nielsen, A., Sigurdsson, S., Hansen, L., Arenas-Garcia, J.: On the relevance of spectral features for instrument classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 2, pp. 485–488, April 2007
Peeters, G., Giordano, B., Susini, P., Misdariis, N., McAdams, S.: The timbre toolbox: extracting audio descriptors from musical signals. J. Acoust. Soc. Am. 130(5), 2902–2916 (2011)
Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Swe, E., Pwint, M.: An efficient approach for classification of speech and music. In: Huang, Y.-M.R., Xu, C., Cheng, K.-S., Yang, J.-F.K., Swamy, M.N.S., Li, S., Ding, J.-W. (eds.) PCM 2008. LNCS, vol. 5353, pp. 50–60. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ubbens, J., Gerhard, D. (2016). Information Rate for Fast Time-Domain Instrument Classification. In: Kronland-Martinet, R., Aramaki, M., Ystad, S. (eds) Music, Mind, and Embodiment. CMMR 2015. Lecture Notes in Computer Science(), vol 9617. Springer, Cham. https://doi.org/10.1007/978-3-319-46282-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-46282-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46281-3
Online ISBN: 978-3-319-46282-0
eBook Packages: Computer ScienceComputer Science (R0)