By Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu
The quantity set LNCS 5263/5264 constitutes the refereed lawsuits of the fifth foreign Symposium on Neural Networks, ISNN 2008, held in Beijing, China in September 2008.
The 192 revised papers awarded have been conscientiously reviewed and chosen from a complete of 522 submissions. The papers are prepared in topical sections on computational neuroscience; cognitive technological know-how; mathematical modeling of neural platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic equipment; supervised studying; unsupervised studying; help vector desktop and kernel equipment; hybrid optimisation algorithms; computing device studying and knowledge mining; clever keep an eye on and robotics; development popularity; audio photograph processinc and computing device imaginative and prescient; fault prognosis; functions and implementations; purposes of neural networks in digital engineering; mobile neural networks and complex keep an eye on with neural networks; nature encouraged tools of high-dimensional discrete info research; development reputation and knowledge 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
Each feature tensor is an array with three modals frequency × time × speaker identity which comprises the cochlear power feature matrix X ∈ RNf ×Nt of different speakers. Then we transform the auditory feature tensor into multiple interrelated subspaces by cNTF to learn the basis functions A(d) , (d = 1, 2, 3). Figure 2 shows the tensor model for the calculation of basis functions. Compared with traditional subspace learning methods, the extracted tensor features may characterize the differences of speakers and preserve the discriminative information for classification.
4, pp. 208–211 (1979) 6. : A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition. EURASIP Journal on Applied Signal Processing 1, 195–209 (2007) 7. : Efficient Auditory Coding. Nature 439, 978–982 (2006) 8. : Learning Self-organized Topology-preserving Complex Speech Features at Primary Auditory Cortex. Neurocomputing 65, 793–800 (2005) 9. : Nonnegative Features of Spectro-temporal Sounds for Classification. Pattern Recognition Letters 26, 1327–1336 (2005) 10.
The discriminative and robust information of different speakers may be preserved after the multi-related subspace projection. Experiment on Aurora2 has shown the improvement of the noise robustness by the new method, in comparison with baseline systems trained on the same amount of information. 2006AA01Z125) and the National Natural Science Foundation of China (Grant No. 60775007). References 1. : Fundamentals on Speech Recognition. Prentice Hall, New Jersey (1996) 2. : RASTA Processing of Speech.
Advances in neural networks - ISNN 2008 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008: proceedings by Fuchun Sun, Jianwei Zhang, Jinde Cao, Wen Yu