Advances in Neural Networks - ISNN 2008: 5th International by Ling Zou, Renlai Zhou, Senqi Hu, Jing Zhang, Yansong Li

By Ling Zou, Renlai Zhou, Senqi Hu, Jing Zhang, Yansong Li (auth.), Fuchun Sun, Jianwei Zhang, Ying Tan, Jinde Cao, Wen Yu (eds.)

The quantity set LNCS 5263/5264 constitutes the refereed complaints of the fifth foreign Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.

The 192 revised papers offered have been rigorously reviewed and chosen from a complete of 522 submissions. The papers are prepared in topical sections on computational neuroscience; cognitive technology; mathematical modeling of neural platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic equipment; supervised studying; unsupervised studying; help vector computer and kernel equipment; hybrid optimisation algorithms; desktop studying and information mining; clever keep an eye on and robotics; development reputation; audio photo processinc and machine imaginative and prescient; fault analysis; functions and implementations; purposes of neural networks in digital engineering; mobile neural networks and complicated keep an eye on with neural networks; nature encouraged tools of high-dimensional discrete information research; trend attractiveness and data processing utilizing neural networks.

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Additional info for Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008, Proceedings, Part I

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Computer screen with a refresh rate of 60Hz. 5m in front of the viewer. Each picture was presented for 1000 ms, with inter-trial intervals varying between 2500 and 3000 ms. After the EEG recordings of each type emotion stimuli, subjects were asked to rate the respective picture on a 11-point scale, among them, 0 means no sense while 100 means very pleasant or unpleasant. 01-100Hz; SYNAMPS, Neuroscan) and digitized at a rate of 500 Hz. 1 s pre- to 1 s poststimulation). Horizontal and vertical electrooculograms (EOG) were recorded by electrodes placed above and below the left eye (VEOG) and lateral to the outer canthus of each eye (HEOG).

Aurora2 speech corpus is used to test the recognition performance, which is designed to evaluate speech recognition algorithms in noisy conditions. Different noise classes were considered to evaluate the performance of ANTF against MFCC, MelNMF, Mel-PCA feature and identification accuracy was assessed. In our experiments the sampling rate of speech signals was 8kHz. For the given speech signals, we employed time window of length 40000 samples (5s). 25ms). Then the dimension of the speaker data is 36 × 10 = 360.

We can observe from Table 1 that the performance degradation of ANTF is slower with increasing noise intensity that compared with other features. It performs better than other three features in the high noise conditions such as 5dB condition noise. Figure 4 describes the identification rate in four noisy conditions averaged over SNRs between 5-20 dB, and the overall average accuracy across all the conditions. The results suggest that this auditory-based tensor representation feature is robust against the additive noise, which indicates the potential of the new feature for dealing with a wider variety of noisy conditions.

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