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Implementation of Linear Prediction Coefficients in G.729E Using VHDL for Man Mission Applications

Publication Type : Conference Proceedings

Publisher : 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)

Source : 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, Tirunelveli, India (2019)

Url : https://ieeexplore.ieee.org/document/8987855

Campus : Bengaluru

School : School of Engineering

Department : Electronics and Communication

Year : 2019

Abstract : Effective audio communication is essential in supporting space flight programs. Information exchange between space and ground links plays a vital role to ensure a successful space mission. However, the availability of bandwidth for data transmission is mission specific. A compression technique, which achieves data compression by removing data redundancy, is used to reduce transmission bandwidth required for huge data transfer. On comparing various Consultative Committees for Space Data Systems (CCSDS) audio compression standards, it has been inferred that the speech codec G.729E manages to achieve “toll” quality speech. The bit rate for this codec is as low as 8 Kbps. It delivers audio signals in small packets of only 10ms time duration. A compression ratio of 16:1 can be achieved using this codec. The algorithm used in G.729E is Conjugate-Structure Algebraic Code Excited Linear Prediction (CS-ACELP) for extracting speech-characterizing parameters. Implementation of this highly complex algorithm on an FPGA would reduce processing delays through parallel processing and pipelining. In this paper linear prediction coefficient in G.729E encoder is implemented on a space graded FPGA RTAX2000S.

Cite this Research Publication : P. G. Nidhi, Dakshayani, S. P. Deeksha, Sushma, S., Neelima, V., and Ms. Priya B. K., “Implementation of Linear Prediction Coefficients in G.729E Using VHDL for Man Mission Applications”, in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2019.

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