SavedAdd your journal

Journals / Turkish Journal of Electrical Engineering and Computer Science / 2012 / Cilt: 20 - Sayı: 6

Skewed alpha-stable distributions for modeling and classification of musical instruments

Mehmet Erdal ÖZBEK
Mehmet Emre ÇEK
Ferit Acar SAVACI
Pages
934–947
Publication Date
2012-06-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades.
📊
Key Findings & Data:Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance.
💡
Conclusion & Impact:We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Musical instrument classification · skewed alpha-stable distribution · generalized Gaussian density

Abstract

Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades. Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance. In this paper, we propose to use the parameters of skewed alpha-stable distribution of sub-band wavelet coefficients of musical sounds as features and show the effectiveness of this new feature set for musical instrument classification. We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers.

Özet

Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades. Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance. In this paper, we propose to use the parameters of skewed alpha-stable distribution of sub-band wavelet coefficients of musical sounds as features and show the effectiveness of this new feature set for musical instrument classification. We compare the classification performance with the features constructed from the parameters of generalized Gaussian density and some of the state-of-the-art features using support vector machine classifiers.

🔖

Cite This Article

ÖZBEK, M. E., ÇEK, M. E., SAVACI, F. A. (2012). Skewed alpha-stable distributions for modeling and classification of musical instruments. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 20 - Sayı: 6, 934–947. https://ajindex.com/articles/188721

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. A. Klapuri, M. Davy (Eds.), Signal Processing Methods for Music Transcription, Springer, 2006.
  2. S. Essid, G. Richard, B. David, ”Instrument recognition in polyphonic music based on automatic taxonomies”, IEEE Trans. on Audio, Speech, and Language Processing, vol. 14, no. 1, pp. 68-80, 2006. B. Kostek, ”Musical instrument classiŞcation and duet analysis employing music information retrieval techniques”, Proceedings of IEEE, vol. 92, no. 4, pp. 712-729, 2004.
  3. P. Herrera-Boyer, G. Peeters, S. Dubnov, ”Automatic classiŞcation of musical instrument sounds”, Journal of New Music Research, vol. 32, no. 1, pp. 3-21, 2003.
  4. W. J. Pielemeier, G. H. WakeŞeld, M. H. Simoni, ”Time-frequency analysis of musical signals”, Proc. of IEEE, vol., no. 9, pp. 1216-1230, 1996.
  5. I. Guyon, A. Elisseeff, ”An introduction to variable and feature selection”, Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
  6. J. D. Deng, C. Simmermacher, S. CraneŞeld, ”A study on feature analysis for musical instrument classiŞcation”, IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 38, no. 2, pp. 429-438, 2008.
  7. S. Wegener, M. Haller, J. Burred, T. Sikora, S. Essid, G. Richard, ”On the robustness of audio features for musical instrument classiŞcation”, In: 16thEuropean Signal Processing Conference, Lausanne, Switzerland, 2008.
  8. Q. Ding, N. Zhang, ”ClassiŞcation of recorded musical instruments sounds based on neural networks”, In: Proc. of the IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, Hawaii, USA, pp. 162, 2007.
  9. J. Eggink, G. J. Brown, ”Instrument recognition in accompanied sonatas and concertos”, In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, Montreal, Canada, vol. 4, pp. 217-220, 2004.
  10. J. R. De Gruijl, M. A. Wiering, ”Musical instrument classiŞcation using democratic liquid state machines”, In: Y. Saeys, E. Tsiporkova, B. D. Baets, Y. V. de Peer (Eds.), 15thBelgian-Dutch Conference on Machine Learning, pp. 40, 2006.
  11. J. Marques, P. J. Moreno, ”A study of musical instrument classiŞcation using Gaussian mixture models and support vector machines”. Technical Report Series CRL 99/4, Compaq Corporation, Cambridge Research Laboratory, 1999.
  12. A. Wieczorkowska, A. Kubik-Komar, ”Application of analysis of variance and post hoc comparisons to studying the discriminative power of sound parameters in distinguishing between musical instruments”, Journal of Intelligent Information Systems, vol.37