Abstract
At a time when the quantity of music media surrounding us is rapidly increasing and the access to recordings as well as the amount of music files available on the Internet is constantly growing, the problem of building music recommendation systems is of great importance. In this work, we perform a study on automatic classification of musical instruments. We use monophonic sounds. The latter have successfully been classified in the past, with main focus on pitch. We propose new temporal features and incorporate timbre descriptors. The advantages of this approach are: preservation of temporal information and high classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brown, J.C.: Musical instrument identification using pattern recognition with cepstral coefficients as features. Journal of Acousitcal society of America 105(3), 1933–1941 (1999)
Casey, M.A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M.: Content-Based Music Information Retrieval: Current Directions and Future Challenges. Proceedings of the IEEE 96(4), 668–696 (2008)
Cosi, P.: Auditory modeling and neural networks. In: Course on Speech Processing, Recognition, and Artificial Neural Networks. Springer, Heidelberg (1998)
Grekow, J., Ras, Z.W.: Detecting Emotion in Classical Music from MIDI Files, Foundations of Intelligent Systems. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS (LNAI), vol. 5722, pp. 261–270. Springer, Heidelberg (2009)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations, New Zealand 11(1) (2009)
Jensen, K., Arnspang, J.: Binary decision tree classification of musical sounds. In: Proceedings of International Computer Music Conference, Beijing, China (1999)
Jiang, W., Cohen, A., Ras, Z.W.: Polyphonic music information retrieval based on multi-label cascade classification system. In: Ras, Z.W., Ribarsky, W. (eds.) Advances in Information and Intelligent Systems. SCI, vol. 251, pp. 117–137. Springer, Heidelberg (2009)
Martin, K.D., Kim, Y.E.: Musical instrument identification: A pattern recognition approach. In: Proceedings of Meeting of the Acoustical Society of America, Norfolk, VA (1998)
Opolko, F., Wapnick, J.: MUMS-McGillUniversityMasterSamples.CD’s (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tzacheva, A.A., Bell, K.J. (2010). Music Information Retrieval with Temporal Features and Timbre. In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-15470-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15469-0
Online ISBN: 978-3-642-15470-6
eBook Packages: Computer ScienceComputer Science (R0)