Advanced Speech Compression VIA Voice Excited Linear Predictive Coding using Discrete Cosine Transform (DCT)
Nikhil Sharma1, Niharika Mehta2

1Nikhil Sharma, (Research Scholar) Nit Kurukshetra, India.
2Niharika Mehta, (Research Scholar) Mriu Faridabad, India.

Manuscript received on 07 February 2013 | Revised Manuscript received on 21 February 2013 | Manuscript Published on 28 February 2013 | PP: 144-148 | Volume-2 Issue-3, February 2013 | Retrieval Number: C0472022313/2013©BEIESP
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Abstract: One of the most powerful speech analysis techniques is the method of linear predictive analysis. This method has become the predominant technique for representing speech for low bit rate transmission or storage. The importance of this method lies both in its ability to provide extremely accurate estimates of the speech parameters and in its relative speed of computation. The basic idea behind linear predictive analysis is that the speech sample can be approximated as a linear combination of past samples. The linear predictor model provides a robust, reliable and accurate method for estimating parameters that characterize the linear, time varying system. In this project, we implement a voice excited LPC vocoder for low bit rate speech compression.
Keywords: Autocorrelation, Discrete Cosine Transform, Levinson Durbin Recursion, Linear predictive coding (LPC).

Scope of the Article: Discrete Optimization