By Malay Kumar Kundu, Durga Prasad Mohapatra, Amit Konar, Aruna Chakraborty
Advanced Computing, Networking and Informatics are 3 precise and at the same time unique disciplines of information without obvious sharing/overlap between them. although, their convergence is saw in lots of genuine global purposes, together with cyber-security, web banking, healthcare, sensor networks, cognitive radio, pervasive computing amidst many others. This two-volume complaints discover the mixed use of complex Computing and Informatics within the subsequent iteration instant networks and defense, sign and photo processing, ontology and human-computer interfaces (HCI). the 2 volumes jointly contain 148 scholarly papers, that have been authorized for presentation from over 640 submissions within the second foreign convention on complicated Computing, Networking and Informatics, 2014, held in Kolkata, India in the course of June 24-26, 2014. the 1st quantity contains cutting edge computing options and suitable learn leads to informatics with selective purposes in development popularity, signal/image processing and HCI. the second one quantity however demonstrates the potential scope of the computing recommendations and informatics in instant communications, networking and security.
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Extra info for Advanced Computing, Networking and Informatics- Volume 1: Advanced Computing and Informatics Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014)
So ﬁnding a matching speech clip in the database to a query clip provided, can be thought of as ﬁnding the clip having the similar sounds in the same sequence. But since the query can be provided by any person, the two sequences will not be the exact duplicate of each other. This problem of matching the sequence of sounds can be mapped to the well known problem of determining the longest common subsequence from two sequences of characters. The only diﬀerence is that in place of characters there are sounds.
4 shows the true estimation of secondary path impulse response with the variation in coefficient value for the true, estimated and error signal with respect to time. The performance of FXLMS algorithm for ANC can be judged from the discussed results pertaining to an adaptive framework. Fig. 2. Primary path impulse response Fig. 3. True secondary path impulse response An Improved Filtered-x Least Mean Square Algorithm for Acoustic Noise Suppression 31 Fig. 4. True estimation of Secondary path impulse response 5 Conclusion FXLMS Algorithm is widely used in acoustic noise cancellation environment due to its simple real time implementation & low computational complexity.
FXLMS algorithm can also be used in very high noise environment. References 1. : Signal Processing for Active Control. Academic Press, London (2001) 2. : Active noise control. IEEE Signal Processing Magazine 10, 12– 35 (1993) 3. : Process of silencing sound oscillations. S. Patent, 2043416 (1936) 4. : Active Noise Control Systems-Algorithms and DSP Implementations. Wiley (1996) 5. : Active Noise Control: A tutorial review. Proceedings of IEEE 87, 943–973 (1999) 6. : Adaptive Signal Processing. Prentice Hall, New Jersey (1985) 7.