By N. R. Shetty, N. H. Prasad, N. Nalini
This complaints quantity covers the court cases of ERCICA 2015. ERCICA offers an interdisciplinary discussion board for researchers, specialist engineers and scientists, educators, and technologists to debate, debate and advertise study and know-how within the upcoming components of Computing, details, verbal exchange and their functions. The contents of this e-book hide rising examine components in fields of Computing, info, conversation and functions. this can end up beneficial to either researchers and working towards engineers.
Read Online or Download Emerging Research in Computing, Information, Communication and Applications : ERCICA 2015, Volume 3 PDF
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Extra info for Emerging Research in Computing, Information, Communication and Applications : ERCICA 2015, Volume 3
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Pdf by CK Kumbharana—2007 2. , Dr. : Speech processing for marathi numeral recognition using MFCC and DTW features. Int. J. Eng. Res. Appl. (IJERA) ISSN: 2248-9622. In: National Conference on Emerging Trends in Engineering & Technology, VNCET-30 Mar’12 3. : Continuous hindi speech recognition using gaussian mixture HMM. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science (2014) 4. : Development of application specific continuous speech recognition system in Hindi. Sci. Res.
8 Throughput versus pause time for Scenario 2 THROUGHPUT (bps) PAUSE TIME (S) THROUGHPUT vs PAUSE TIME 4000 3900 3800 3700 QSADSR 3600 DSR 3500 3400 3300 0 30 60 90 120 150 Fig. 1 0 0 50 100 150 PAUSE TIME (S) 200 Developing QoS Aware DSR for JiST/SWANS Simulator … PDR Fig. 84 21 PACKET DELIVERY RATIO vs PAUSE TIME QSADSR(10m/s) DSR(10m/s) 0 50 100 150 200 Fig. 11 Throughput (%) versus pause time for Scenario 3 THROUGHPUT (%) PAUSE TIME (S) 102 100 98 96 94 92 90 88 86 84 THROUGHPUT(%) vs PAUSE TIME QSADSR(10m/s) DSR(10m/s) 0 50 100 150 200 Fig.
The delta cepstrum can be used in the time derivatives of the energy of the signal. It is also used for finding the velocity and acceleration of energy with MFCC. The MFCCs can be used as audio classification features to improve classification accuracy in music features [14]. Before MFCCs were introduced, LPCs and linear prediction cepstral coefficients (LPCCs) were the main feature type for ASR [15]. They are used in speaker verification for extracting speaker information such as contents and channels [16].