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Performance Comparison of Musical Instrument Family Classification Using Soft Set
Saima Anwar Lashari, Rosziati Ibrahim, Norhalina Senan
Pages - 100 - 110     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 3   Issue - 4    |    Publication Date - December 2012  Table of Contents
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KEYWORDS
Traditional Pakistani Musical Instruments Sounds, Classification, Soft Set
ABSTRACT
Nowadays, it appears essential to design automatic and efficacious classification algorithm for the musical instruments. Automatic classification of musical instruments is made by extracting relevant features from the audio samples, afterward classification algorithm is used (using these extracted features) to identify into which of a set of classes, the sound sample is possible to fit. The aim of this paper is to demonstrate the viability of soft set for audio signal classification. A dataset of 104 (single monophonic notes) pieces of Traditional Pakistani musical instruments were designed. Feature extraction is done using two feature sets namely perception based and mel-frequency cepstral coefficients (MFCCs). In a while, two different classification techniques are applied for classification task, which are soft set (comparison table) and fuzzy soft set (similarity measurement). Experimental results show that both classifiers can perform well on numerical data. However, soft set achieved accuracy up to 94.26% with best generated dataset. Consequently, these promising results provide new possibilities for soft set in classifying musical instrument sounds. Based on the analysis of the results, this study offers a new view on automatic instrument classification
CITED BY (2)  
1 Mohamed, H., Ahmad, N. B. H., & Shamsuddin, S. M. H. (2014, September). Bijective soft set classification of student's learning styles. In Software Engineering Conference (MySEC), 2014 8th Malaysian (pp. 289-294). IEEE.
2 Lashari, S. A., & Ibrahim, R. (2013). A Framework for Medical Images Classification Using Soft Set. Procedia Technology, 11, 548-556.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
Ali, S. & Smith, K.A. On learning algorithms selection for classification. Applied Soft Computing 6,pages119-138 (2006).
Davis, S.B. and Mermelstein, P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transaction on Acoustics, Speech and Signal Processing, Volume number 28, issue 4:357-366 (1980).
Ding, Q., Zhang, N. Classification of Recorded Musical Instruments Sounds Based on Neural Networks. In: IEEE Symposium on Computational Intelligence in Image and Signal Processing. Pp 157-162. Honolulu, HI (2007).
Essid, S., Richard, G., & David, B. Musical Instruments Recognition by pair wise classification strategies. Audio, Speech and Language processing, IEEE Transaction on Speech and Audio Processing, 14(2), 1401-1412 (2005).
Gunasekaran, S. & Revathy, K. Fractal dimension analysis of audio signals for Indian musical instrument recognition. International Conference on Audio, Language and Image Processing,Shanghai, ISBN: 978-1-4244-1723-0, pp. 257-261 (2008).
Handaga, B. & Deris M. M. Similarity approach on fuzzy soft set based numerical data classification.Communications in Computer and Information Science. Volume 180(6), pp. 575-589 (2011).
Herawan, T. Deris, M. M. A Direct Proof of Every Rough Set is a Soft Set. In Third Asia International Conference on Modeling & Simulation, AMS '09 pages119–124, Bali (2009).
Jiang, Y., Tang, Y. & Chen, Q. An adjustable approach to intuitionistic fuzzy soft sets based decision making. Applied Mathematics Modeling. Volume 35 (2), pp. 824-836 (2011).
Kharal, A. Distance and similarity measurement for soft set. New Math.. & Nat. Com.(NMNC). Volume(6), pp. 321-334 (2010).
Kotsiantis, S.B. Supervised machine learning: A review of classification techniques. Informatica 31,pages 249-268. (2007)
Kumari, R. S. S., Sugumar, D. & Sadasivam,V. Audio signal classification based on optimal wavelet and support vector machine. Proceeding of International conference on computational intelligence and multimedia applications. Volume 2, pp: 544 – 548. (2007)., ISBN:0-7695-3050-8
Liu, J. & Xie, L. Comparison of Performance in Automatic Classification between Chinese and Western Musical Instruments. In: Proceeding of WASE International Conference on Information Engineering. Beidaihe,Hebei, (2010)
Maji, P. K., Biswas, R. & Roy, A. Fuzzy soft sets. Journal of Fuzzy Mathematics 9(3), pp.589-602(2001).
Maji, P. K., Roy, A. R., Biswas, R. An application of soft sets in decision making problem. Computers and mathematics with applications. pp 1077-1083. (2002).
Majumdar, P. & Samantra,S.K.(2010). Generalized fuzzy soft set. Journal of Computational and Applied Mathematics. Pp. 1279-1286.
Marshall, P. (2011). Sachs Hornbostel System of instrument classification. Retrieved December 8,2010 from website http://www.drumdojo.com/sachshornbostel.htm
McKay, C. & Fujinaga, I. Automatic music classification and the importance of instrument identification. In: Proceedings of the Conference on Interdisciplinary Musicology (CIM05). Montreal,Canada (2005).
Mierswa, I., & Morik, K. Automatic Feature Extraction for Classifying Audio Data. Journal of Machine Learning: Volume 58 Issue 2-3 (2005).
Molodtsov, D. Soft set theory –first results. Computer and mathematics with applications. Pp19-31.(1999).
Mushrif, M., M, Sengupta, S., Ray, A.K. Texture Classification Using a Novel Soft Set Theory Based Classification Algorithm. In: LNCS, vol.3851, pp.246-254.Springer, Heidelber (2006).
Roy, A. R. & Maji, P. A fuzzy soft set theoretic approach to decision making problems. Journal of Computational and Applied Mathematics. Volume 203(2), pp.540-542 (2007).
Roy, A. R. & Maji, P. A fuzzy soft set theoretic approach to decision making problems.Journal of Computational and Applied Mathematics. Volume 203(2), pp.540-542 (2007).
Sarimollaoglu, M., Dagtas, S., Iqbal, K., Bayrak, C. A text–independent speaker identification system using probabilistic neural networks. International conference on computing, communications and control technologies (CCCT), Austin, USA, pages 407-411(2004).
Senan, N., Herawan, T., Mokji, M.M., Nawi, N.M., & Ibrahim, R. The Ideal Data Representation for Feature Extraction of Traditional Malay Musical Instrument Sounds Classification. Advanced intelligent computing theories and applications: Lecture Notes in Computer Science, 2010, Volume 6215/2010,pages 345-353
Senan, N., Ibrahim, R, Nawi, N. M., Yanto, I. T. R.& Herawan, T. Feature Selection for Traditional Malay musical instruments sounds classification using rough set. .Journal of Computing Volume 3,Issue 2, (2011).
Tzanetakis, G., Cook, P. Musical genre classification of audio signals. IEEE Transaction on Speech and Audio Processing: Vol. 10, No. 5 (2002).
Wieczorkowska, A. & CzySewski, A. Rough Set Based Automatic Classification of Musical Instrument Sounds. In: International Workshop on Rough Sets in Knowledge Discovery and Soft Computing. pp.298-309, (2003).
Wieczorkowska, A. Rough Sets as a tool for audio signal classification. Lecture Notes in Computer Science, Volume 1609/1999, 367-375, (1999) DOI: 10.1007/BFb0095123.
Wiezorkowska, A. Towards Musical Data Classification via Wavelet Analysis. In Proceeding of the 12th International Symposium on Foundations of Intelligent Systems, Springer–Verlag London Uk.(2000).
Zadeh, L. Fuzzy sets. Inform. Control. Volume number 8: 338-353. (1965).
Ziagham, N. (2003). Forms of Pakistan Music. Retrieved March, 9, 2011 from http://www.maighmalhaar.com/IntroductionPage1.html
Zou, Y.& Xiao, Z. Data analysis approaches of soft sets under incomplete information. KnowledgeBased System 21, 2128-2137 (2008).
Mr. Saima Anwar Lashari
Universiti Tun Hussein Onn Malaysia - Malaysia
hi100008@siswa.uthm.edu.my
Professor Rosziati Ibrahim
- Malaysia
Mr. Norhalina Senan
- Malaysia


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