KaydedilenlerDerginizi ekleyin

Dergiler / 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
Sayfa
934–947
Yayın Tarihi
2012-06-01
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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 Maddede Bu Makale

🎯
Amaç & Hipotez:Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades.
📊
Temel Bulgular & Veri: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.
💡
Sonuç & Katkı: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.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Musical instrument classification · skewed alpha-stable distribution · generalized Gaussian density

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.

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.

🔖

Bu Makaleye Atıf Yap

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

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (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