Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network

Abstract:

The challenging for buyers around the globe to identify good quality of Durian. For several kinds of Durian, it may be difficult for buyers to determine the Durian quality by appearance. The ability to select only good quality Durian without cutting or cleaving is useful because buyers will not waste money ordering undesirable Durian.This paper proposes a nondestructive technique to determine the stages of maturity of durian fruits. The presented methodology utilizes the concept of pattern matching. We used the local knocking equipment to knock the durian for knocked-sound. After that the knocked-sound was analyzed and generated to Mel-frequency cepstral coefficients (MFCCs) that is used to train data for the classifier. Feed-Forward Neural Network was used for the classifier and can effectively classification the stages of maturity of durian fruits with accuracy rate more than 82%.

• Periodical: Applied Mechanics and Materials (Volume 855)
• Pages: 75-81
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.855.75
• Online since: October 2016
• Authors: Peerapol Khunarsa*, Julalug Mahawan, Pisit Nakjai, Nerissa Onkhum
• Keywords: Mel Frequency Cepstral Coefficient (MFCC), Neural Network, Pattern Recognition, Signal Processing

References

[1] R. Phoophuangpairoj, S. Phongsuphap, and S. Tangwongsan, Gender identification from Thai speech signal using a neural network, Lecture Notes in Computer Science, Vol. 5863, 2009, pp.676-684.

DOI: 10.1007/978-3-642-10677-4_77

[2] H. Ting, Y. Yingchun, and W. Zhaohui, Combining MFCC and pitch to enhance the performance of the gender recognition, in Proceedings of International Conference on Signal Processing, 2006, pp.16-20.

DOI: 10.1109/icosp.2006.345541

[3] S. M. R. Azghadi, M. R. Bonyadi, and H. Sliahhosseini, Gender classification based on feedforward backpropagation neural network, IFIP International Federation for Information Processing, Vol. 247, 2007, pp.299-304.

DOI: 10.1007/978-0-387-74161-1_32

[4] M. H. James and J. C. Michael, The role of F0 and formant frequencies in distinguishing the voices of men and women, attention, Perception and Psychophysics, Vol. 71, 2009, pp.1150-1166.

DOI: 10.3758/app.71.5.1150

[5] C. R. Pernet and P. Belin, The role of pitch and timbre in voice gender categorization, Frontiers in Psychology, Vol. 3, Article 23, 2012, pp.1-11.

DOI: 10.3389/fpsyg.2012.00023

[6] M. Sigmund, Gender distinction using short segments of speech signal, International Journal of Computer Science and Network Security, Vol. 8, 2008, pp.159-162.

[7] D. Ververidis and C. Kotropoulos, Automatic speech classification to five emotional states based on gender information, in Proceedings of the European Signal Processing Conference, Vol. 1, 2004, pp.341-344.

DOI: 10.1109/icassp.2004.1326055

[8] A. Deemagarn and A. Kawtrakul, Thai connected digit speech recognition using Hidden Markov Models, in Proceedings of the 9th International Conference on Speech and Computer, 2004, pp.731-735.

[9] L. Fuhai, M. Jinwen, and D. Huang, MFCC and SVM based recognition of Chinese vowels, Lecture Notes in Computer Science, Vol. 3802, 2005, pp.812-819.

DOI: 10.1007/11596981_118

[10] S. Tangwongsan and R. Phoophuangpairoj, Boosting Thai syllable speech recognition using acoustic models combination, in Proceedings of International Conference on Computer and Electrical Engineering, 2008, pp.568-572.

DOI: 10.1109/iccee.2008.130

[11] S. Tangruamsub, P. Punyabukkana, and A. Suchato, Thai speech keyword spotting using heterogeneous acoustic modeling, in Proceedings of IEEE International Conference on Research, Innovation and Vision for the Future, 2007, pp.253-260.

DOI: 10.1109/rivf.2007.369165

[12] S. Tangwongsan, P. Po-Aramsri, and R. Phoophuangpairoj, Highly efficient and effective techniques for Thai syllable speech recognition, Lecture Notes in Computer Science, Vol. 3321, 2004, pp.259-270.

DOI: 10.1007/978-3-540-30502-6_19

[13] R. Phoophuangpairoj, Using multiple HMM recognizers and the maximum method to improve voice-controlled robots, in Proceedings of International Conference on Intelligent Signal Processing and Communication Systems, 2011, pp.1-6.

DOI: 10.1109/ispacs.2011.6146111

[14] G. Guo and S. Z. Li, Content-based audio classification and retrieval by support vector machines, IEEE Transactions on Neural Networks, Vol. 14, 2003, pp.209-215.

DOI: 10.1109/tnn.2002.806626

[15] D. Mitrovic, M. Zeppelzauer, and C. Breiteneder, Discrimination and retrieval of animal sounds, in Proceedings of the 12th International Multi-Media Modelling Conference, 2006, pp.339-343.

DOI: 10.1109/mmmc.2006.1651344

[16] C. Y. Yeo, S. A. R. Al-Haddad, and C. K. Ng, Animal voice recognition for identification (ID) detection system, in Proceedings of the 7th IEEE International Colloquium on Signal Processing and Its Applications, 2011, pp.198-201.

DOI: 10.1109/cspa.2011.5759872

[17] Motlíček P.: Feature Extraction in Speech Coding and Recognition, Report, Portland, to research, data, and theory. Belmont, CA: Thomson/Wadsworth, 2003 US, Oregon Graduate Institute of Science and Technology, pp.1-50, (2002).

[18] Hagen A., Connors D.A. & Pellm B.L.: The Analysis and Design of Architecture Systems for Speech Recognition on Modern HandheldComputing Devices. Proceedings of the 1st IEEE/ACM/IFIP international conference on hardware/software design and system synthesis, pp.65-70, (2003).

DOI: 10.1145/944658.944661

[19] Ishizuka K. & Nakatani T.: A feature extraction method using subband based periodicity and aperiodicity decomposition with noise robust frontend processing for automatic speech recognition. Speech Communication, Vol. 48, Issue 11, pp.1447-1457, (2006).

DOI: 10.1016/j.specom.2006.06.008