Yıl: 2012 Cilt: 20 Sayı: 6 Sayfa Aralığı: 934 - 947 Metin Dili: İngilizce İndeks Tarihi: 29-07-2022

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

Öz:
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.
Anahtar Kelime:

Konular: Mühendislik, Elektrik ve Elektronik
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [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.
  • [3] B. Kostek, ”Musical instrument classification and duet analysis employing music information retrieval techniques”, Proceedings of IEEE, vol. 92, no. 4, pp. 712-729, 2004.
  • [4] P. Herrera-Boyer, G. Peeters, S. Dubnov, ”Automatic classification of musical instrument sounds”, Journal of New Music Research, vol. 32, no. 1, pp. 3-21, 2003.
  • [5] W. J. Pielemeier, G. H. Wakefield, M. H. Simoni, ”Time-frequency analysis of musical signals”, Proc. of IEEE, vol. 84, no. 9, pp. 1216-1230, 1996.
  • [6] I. Guyon, A. Elisseeff, ”An introduction to variable and feature selection”, Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
  • [7] J. D. Deng, C. Simmermacher, S. Cranefield, ”A study on feature analysis for musical instrument classification”, IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 38, no. 2, pp. 429-438, 2008.
  • [8] S. Wegener, M. Haller, J. Burred, T. Sikora, S. Essid, G. Richard, ”On the robustness of audio features for musical instrument classification”, In: 16th European Signal Processing Conference, Lausanne, Switzerland, 2008.
  • [9] Q. Ding, N. Zhang, ”Classification 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. 157-162, 2007.
  • [10] 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.
  • [11] J. R. De Gruijl, M. A. Wiering, ”Musical instrument classification using democratic liquid state machines”, In: Y. Saeys, E. Tsiporkova, B. D. Baets, Y. V. de Peer (Eds.), 15th Belgian-Dutch Conference on Machine Learning, pp. 33-40, 2006.
  • [12] J. Marques, P. J. Moreno, ”A study of musical instrument classification using Gaussian mixture models and support vector machines”. Technical Report Series CRL 99/4, Compaq Corporation, Cambridge Research Laboratory, 1999.
  • [13] 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, no. 3 pp. 293-314. doi:10.1007/s10844-010-0140-5, 2011.
  • [14] A. Wieczorkowska, ”Musical sound classification based on wavelet analysis”, Fundamenta Informaticae, vol. 47, no. 1-2, pp. 175-188, 2001.
  • [15] M. N. Do, M. Vetterli, ”Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance”, IEEE Trans. on Image Processing, vol. 11, pp. 146-158, 2002.
  • [16] T. Li, M. Ogihara, ”Toward intelligent music information retrieval”, IEEE Trans. on Multimedia, vol. 8, no. 3, pp. 564-574, 2006.
  • [17] C. Tzagkarakis, A.Mouchtaris, P. Tsakalides, ”Musical genre classification via generalized Gaussian and alpha-stable modeling”, In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Toulouse, France, vol. 5, pp. 217-220, 2006.
  • [18] M. I. Taroudakis, G. Tzagkarakis, P. Tsakalides, ”Classification of shallow-water acoustic signals via alpha-stable modeling of the one-dimensional wavelet coefficients”, Journal of Acoustical Society of America, vol. 119, no. 3, pp. 1396-1405, 2006.
  • [19] M. E. Ozbek, F. A. Savacı, ”Music instrument classification using Generalized Gaussian density modeling”, In: IEEE 15th Signal Processing and Communications Applications Conference, Eski¸sehir, Turkey, 2007.
  • [20] G. Samorodnitsky, M. S. Taqqu, Stable Non-Gaussian Random Processes: Stochastic models with infinite variance, Chapman and Hall/CRC, 2000.
  • [21] P. Kidmose, ”Alpha-stable distributions in signal processing of audio signals”, In: Proc. of the 41st Conference on Simulation and Modelling, Scandinavian Simulation Society, pp. 87-94, 2000.
  • [22] E. E. Kuruoglu, J. Zerubia, ”Skewed α-stable distributions for modelling textures”, Pattern Recognition Letters, vol. 24, pp. 339-348, 2003.
  • [23] D. Salas-Gonzalez, E. E. Kuruoglu, D. P. Ruiz, ”A heavy-tailed empirical Bayes method for replicated microarray data”, Computational Statistics and Data Analysis, vol. 53, pp. 1535-1546, 2009.
  • [24] E. E. Kuruoglu, ”Density parameter estimation of skewed α-stable distributions”, IEEE Trans. on Signal Processing, vol. 49, no. 10, pp. 2192-2201, 2001.
  • [25] S. G. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way (3rd Edition), Academic Press, 2009.
  • [26] M. Vetterli, J. Kovaˇcevi´c, Wavelets and Subband Coding, Prentice Hall, 1995.
  • [27] L. Fritts, ”The University of Iowa Electronic Music Studios Musical Instrument Samples”, http://theremin.music.uiowa.edu, 1997.
  • [28] M. E. Ozbek, C. Delpha, P. Duhamel, ”Musical note and instrument classification with likelihood-frequency-time analysis and support vector machines”, In: 15th European Signal Processing Conference, Poznan, Poland, pp. 941-945, 2007.
  • [29] B. Kostek, Perception-Based Data Processing in Acoustics, Springer, 2005.
  • [30] O. Lartillot, P. Toiviainen, ”A MATLAB toolbox for musical feature extraction from audio”, In: Proc. of the 10th International Conference on Digital Audio Effects, Bordeaux, France, pp. 237–244, 2007.
  • [31] M. Casey, ”MPEG-7 sound-recognition tools”, IEEE Trans. on Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 737-747, 2001.
  • [32] ISO/IEC Working Group, ”MPEG-7 overview”, http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg- 7.htm, 2004.
  • [33] V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
  • [34] B. Schölkopf, A. J. Smola, Learning with Kernels, MIT Press, 2002.
APA Özbek M, ÇEK M, SAVACI F (2012). Skewed alpha-stable distributions for modeling and classification of musical instruments. , 934 - 947.
Chicago Özbek Mehmet Erdal,ÇEK MEHMET EMRE,SAVACI Ferit Acar Skewed alpha-stable distributions for modeling and classification of musical instruments. (2012): 934 - 947.
MLA Özbek Mehmet Erdal,ÇEK MEHMET EMRE,SAVACI Ferit Acar Skewed alpha-stable distributions for modeling and classification of musical instruments. , 2012, ss.934 - 947.
AMA Özbek M,ÇEK M,SAVACI F Skewed alpha-stable distributions for modeling and classification of musical instruments. . 2012; 934 - 947.
Vancouver Özbek M,ÇEK M,SAVACI F Skewed alpha-stable distributions for modeling and classification of musical instruments. . 2012; 934 - 947.
IEEE Özbek M,ÇEK M,SAVACI F "Skewed alpha-stable distributions for modeling and classification of musical instruments." , ss.934 - 947, 2012.
ISNAD Özbek, Mehmet Erdal vd. "Skewed alpha-stable distributions for modeling and classification of musical instruments". (2012), 934-947.
APA Özbek M, ÇEK M, SAVACI F (2012). Skewed alpha-stable distributions for modeling and classification of musical instruments. Turkish Journal of Electrical Engineering and Computer Sciences, 20(6), 934 - 947.
Chicago Özbek Mehmet Erdal,ÇEK MEHMET EMRE,SAVACI Ferit Acar Skewed alpha-stable distributions for modeling and classification of musical instruments. Turkish Journal of Electrical Engineering and Computer Sciences 20, no.6 (2012): 934 - 947.
MLA Özbek Mehmet Erdal,ÇEK MEHMET EMRE,SAVACI Ferit Acar Skewed alpha-stable distributions for modeling and classification of musical instruments. Turkish Journal of Electrical Engineering and Computer Sciences, vol.20, no.6, 2012, ss.934 - 947.
AMA Özbek M,ÇEK M,SAVACI F Skewed alpha-stable distributions for modeling and classification of musical instruments. Turkish Journal of Electrical Engineering and Computer Sciences. 2012; 20(6): 934 - 947.
Vancouver Özbek M,ÇEK M,SAVACI F Skewed alpha-stable distributions for modeling and classification of musical instruments. Turkish Journal of Electrical Engineering and Computer Sciences. 2012; 20(6): 934 - 947.
IEEE Özbek M,ÇEK M,SAVACI F "Skewed alpha-stable distributions for modeling and classification of musical instruments." Turkish Journal of Electrical Engineering and Computer Sciences, 20, ss.934 - 947, 2012.
ISNAD Özbek, Mehmet Erdal vd. "Skewed alpha-stable distributions for modeling and classification of musical instruments". Turkish Journal of Electrical Engineering and Computer Sciences 20/6 (2012), 934-947.