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Optimized Neural Network Architecture for The Classification of Voice Signals

Dipak D. Shudhalwar1 , Ganesh Kumar Dixit2 , Pallavi Agrawal3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 502-506, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.502506

Online published on Sep 30, 2018

Copyright © Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal, “Optimized Neural Network Architecture for The Classification of Voice Signals,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.502-506, 2018.

MLA Style Citation: Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal "Optimized Neural Network Architecture for The Classification of Voice Signals." International Journal of Computer Sciences and Engineering 6.9 (2018): 502-506.

APA Style Citation: Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal, (2018). Optimized Neural Network Architecture for The Classification of Voice Signals. International Journal of Computer Sciences and Engineering, 6(9), 502-506.

BibTex Style Citation:
@article{Shudhalwar_2018,
author = {Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal},
title = {Optimized Neural Network Architecture for The Classification of Voice Signals},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {502-506},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2899},
doi = {https://doi.org/10.26438/ijcse/v6i9.502506}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.502506}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2899
TI - Optimized Neural Network Architecture for The Classification of Voice Signals
T2 - International Journal of Computer Sciences and Engineering
AU - Dipak D. Shudhalwar, Ganesh Kumar Dixit, Pallavi Agrawal
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 502-506
IS - 9
VL - 6
SN - 2347-2693
ER -

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Abstract

In this paper, the performance to optimize feed-forward neural network has been evaluated for the classification of voice signals of English alphabets. There are various feed forward neural network models have been used earlier but the selection of optimize architecture is a challenge. In this paper we are implementing a optimize architecture which is best suitable for the classification of voice signals. Digital signal processing operations are applied on analog speech signals to convert them into digital form and then to make them suitable for further processing by neural network models.

Key-Words / Index Term

Digital signal processing, Optimize neural network, Pattern classification

References

[1]. P. Rani, S. Kakkar and S. Rani, “Speech Recognition Using Neural Network”, In Proceedings of International Conference on Advancements in Engineering and Technology, International Journal of Computer Applications, pp. 11-14, 2015.
[2]. M. A. Anusuya and S. K. Katti, “Speech Recognition by Machine: A Review”, International Journal of Computer Science and Information Security, pg. 181 – 205, Vol. 6, No. 3, 2009.
[3]. X. Cui et.al., “A Study of Variable-Parameter Gaussian Mixture Hidden Markov Modeling for Noisy Speech Recognition”, IEEE Transactions On Audio, Speech, And Language Processing, Vol. 15, No. 4, 2007.
[4]. G.E. Dahl, M. Ranzato, A. Mohamed and G.E. Hinton, “Phone Recognition with the Mean-covariance Restricted Boltzmann Machine”, Adv. Neural Inf. Process. Syst., No. 23, 2010.
[5]. D. Yu, L. Deng and G. Dahl, “Roles of Pre-training and Fine-tuning in Context-dependent DBN-HMMs for Real-world Speech Recognition”, In Proceedings of NIPS Workshop Deep Learn, Unsupervised Feature Learn, 2010.
[6]. H. Bourland and C.J. Wellekens, “Multilayer Perceptrons and Automatic Speech Recognition”, IEEE First International Conference on Neural Networks, San Diego, California IV-407-IV-416, June 21-24, 1987.
[7]. H. Yashwanth, H. Mahendrakar and S. David, “Automatic Speech Recognition Using Audio Visual Cues”, IEEE India Annual Conference, pp. 166-169, 2004.
[8]. Robinson and F. Fallside, “A Recurrent Error Propagation Network Speech Recognizer System”, Computer, Speech and Language, Vol. 5, No. 3, 1991.
[9]. L. Yang and Z. Yang, “Study on Audio Signal’s Classification Based on BP Neural Network”, IEEE Conference Publications on Artificial Intelligence, Management Science and Electronic Commerce, pp. 5153-5155, 2011.
[10]. S. Balochian, E. A. Seidabadand S. Z. Rad, “ Neural Network Optimization Genetic Algorithms for the Audio Classification to Speech and Music”, International Journal of Signal Processing, Image Processing and Pattern Recognition, pg. 47-54, Vol. 6, No. 3, 2013.
[11]. T. Lefteri H. and A. U. Robert, “Fuzzy and Neural Approaches in Engineering”, John Wiley and Sons Publications, 1997.
[12]. R. Hecht-Nielsen, “Theory of Backpropagation Neural Network”, International Joint Conference on Neural Networks, pp. 593-605, Vol. 1, 1989.
[13]. M.J.D. Powell, “Radial Basis Functions for Multivariate Interpolation: A Review”, In Algorithms for the Approximation of Functions and Data, J.C. Mason and M.G. Cox, eds., Clarendon Press, pp. 143-167, 1987.
[14]. S. N. Parappa and M. P. Singh, “Conjugate Descent of Gradient Descent Radial Basis Function for Generalization of Feed-forward Neural Network”, International Journal of Advancements in Research & Technology, pg. 112-125, Vol. 2, Issue 12, 2013.