By Subhransu Sekhar Dash, M. Arun Bhaskar, Bijaya Ketan Panigrahi, Swagatam Das
The booklet is a suite of fine quality peer-reviewed learn papers awarded within the first overseas convention on overseas convention on synthetic Intelligence and Evolutionary Computations in Engineering platforms (ICAIECES -2015) held at Velammal Engineering collage (VEC), Chennai, India in the course of 22 – 23 April 2015. The ebook discusses big variety of business, engineering and medical functions of the rising options. Researchers from educational and current their unique paintings and trade rules, details, ideas and functions within the box of verbal exchange, Computing and gear Technologies.
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Extra resources for Artificial Intelligence and Evolutionary Computations in Engineering Systems: Proceedings of ICAIECES 2015
008 is set for the noise samples taken for simulation. 008 has been considered as only noise and been processed such that their enhanced amplitude is 0. N. M. Ravikumar [Iteration] If SNRI ðnÞ SNRo ðnÞ jj SNRo ðnÞ \ 0 then If Rs ðnÞ \ Z Then d2r ðnÞ ¼ 0 Else Rs ðnÞ ¼ d2x ðnÞ End Rs ðnÞ Rs ðnÞ þ Rv ðnÞ SðnÞ ¼ K ðnÞXyðnÞ K ð nÞ ¼ where Rs ðnÞ ¼ Variance of noisy speech, Rv ðnÞ ¼ Variance of noise, d2r ðnÞ ¼ variance of speech frame, K(n) = Kalman gain, S(n) = Enhanced speech. 3 Implementation Steps and Results The prerequisite for the implementation of the algorithm is the creation of the database for the noise and the speech.
Improving application placement for cluster-based web applications. IEEE Trans Netw Serv Manag. 2011;8:2. N. M. Ravikumar Abstract Kalman Filtering is one of the time domain speech-enhancement techniques. The conventional Kalman ﬁltering technique involves more number of matrix operations. The complexity of matrix operations is reduced by fast adaptive Kalman Filtering technique. The proposed method of fast adaptive ﬁltering technique is simple and gives best results for stationary noises. From the simulation results, it is seen that the proposed method of Kalman ﬁltering is more effective in obtaining the clean speech signal.
Ravikumar [Iteration] If SNRI ðnÞ SNRo ðnÞ jj SNRo ðnÞ \ 0 then If Rs ðnÞ \ Z Then d2r ðnÞ ¼ 0 Else Rs ðnÞ ¼ d2x ðnÞ End Rs ðnÞ Rs ðnÞ þ Rv ðnÞ SðnÞ ¼ K ðnÞXyðnÞ K ð nÞ ¼ where Rs ðnÞ ¼ Variance of noisy speech, Rv ðnÞ ¼ Variance of noise, d2r ðnÞ ¼ variance of speech frame, K(n) = Kalman gain, S(n) = Enhanced speech. 3 Implementation Steps and Results The prerequisite for the implementation of the algorithm is the creation of the database for the noise and the speech. The noise database is created by considering various acoustic environmental noises.
Artificial Intelligence and Evolutionary Computations in Engineering Systems: Proceedings of ICAIECES 2015 by Subhransu Sekhar Dash, M. Arun Bhaskar, Bijaya Ketan Panigrahi, Swagatam Das