Improvement Voiced and Unvoiced Classification Technique Based on Real Time Processing Using FPGA Board
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Abstract
In speech signal processing, the amount of data analyzing requires a long time process. One of the pre-processing techniques to make the speech processing faster is the voiced and unvoiced (V/UV) classification. This article presents an improvement V/UV classification technique based on real time processing using Field Programmable Gate Array (FPGA) board. The Virtex-II Pro board which consists of XC2VP30 chip as central processor unit is used in this experiment. The XC2VP30 chip consists of 30,816 logic cells and it can operate with external memory. The experiment results show that this system can be function on real time system. It used only 4.17-50 ms for time processing which does not effect to delay time process especially in real time system. Moreover, the output speech signal quality is still similar to the original speech signal. This is the major point that the XC2VP30 chip can be develop to use in speech compression and speech recognition.
Keywords: Voiced and unvoiced classification, Xilinx XC2VP30, Real time processing system
Email: Jakkree.s@en.rmutt.ac.th
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References
[2] Srinonchat, J. Comparison of the Efficiency of Ordered and Disordered Codebook Techniques in Speech Coding. IEEE International Conference on Information and Communication Systems, 2005, pp. 195 – 198.
[3] Srinonchat, J. Enhancement Artificial Neural Networks for Low-Bit Rate Speech Compression system. IEEE International Symposium on Communications and Information Technologies, 2006, pp. 195-198.
[4] Jung, U, C., Lee, R, D., Park, H. J., Kim, H, S., Lee, C, H., Choi, S, Jung. and Jeon, W, J. An FPGA-
Based Voice Signal Preprocessor for The Real-Time Cross-Correlation. International Conference on Control, Automation and Systems, 2007, pp. 793 – 797.
[5] Rahman, U, M, T. and Rashid, A. B. M. H. FPGA Implementation of Real Time Acoustic Noise Suppression by Spectral Subtraction using Dynamic Moving Average Method. IEEE Symposium on Industrial Electronics and Applications, 2009, pp. 365 – 370.
[6] Xu, J., Ariyaeeinia, A. and Sotudeh, R. User Voice Idenfication on Fpga. Perspectives in Pervasive Computing, 2005, pp. 75 - 83.
[7] Sutacha, C. and Srinonchat, J. Improvement Speech Signal Real Time Processing Techniques Using FPGA Board. Electical engineering network 2011 of rajamangala university of technology 2012, pp.585-588.
[8] Jung, J., Jin, S., Kim, D., Kim, H, S., Choi, K, S. and Joen, W, J. A Voice Activity Detection System Based on FPGA. International Conference on Control, Automation and Systems, 2010, pp. 2304 - 2308
[9] Pattanaburi, K. and Srinonchat, J. Enhancement Pattern Analysis Technique for Voiced/Unvoiced Classification. IEEE-International symposium on computer, consumer and control, 2012, pp. 389-392