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 offered have been conscientiously 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 tools; supervised studying; unsupervised studying; aid vector computer and kernel tools; hybrid optimisation algorithms; computing device studying and information mining; clever regulate and robotics; trend reputation; audio snapshot processinc and laptop imaginative and prescient; fault analysis; functions and implementations; functions of neural networks in digital engineering; mobile neural networks and complex keep watch over with neural networks; nature encouraged tools of high-dimensional discrete info research; development 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
The 18 Q. Wu, L. Zhang, and G. Shi Table 1. 4 testing data were mixed with subway, babble, car noise, exhibition hall in SNR intensities of 20dB, 15dB, 10dB and 5dB. For the final feature set, 16 cepstral coefficients were extracted and used for speaker modeling. GMM was used to build the recognizer with 64 gaussian mixtures. For comparison, the performance of MFCC, Mel-NMF and Mel-PCA with 16-order cepstral coefficients are also tested. We use PCA and NMF to learn the part-based representation in the spectro-temporal domain after mel filtering, which is similar to .
Shi about sparse coding and representation for sound and speech[8,9,10] is also proved to be useful for auditory modeling and speech separation which will be a potential way for robust speech feature extraction. As a powerful data modeling tool for pattern recognition, multilinear algebra of the higher order tensors has been proposed as a potent mathematical framework to manipulate the multiple factors underlying the observations. Currently common tensor decomposition methods include: (1) the CANDECOMP/PARAFAC model [11,12,13]; (2) the Tucker Model[14,15]; (3) Nonnegative Tensor Factorization (NTF) which imposes the nonnegative constraint on the CANDECOMP/PARAFAC model [16,17].
E. A ˆ = [A−1 ]+ . Figure 3(a) where A shows an example of basis functions in spectro-temporal domain. From this result we can see that most elements of basis function are near to zero, which accords with the sparse constraint of cNTF. Figure 3(b) gives several examples for the encoding feature vector after transformation which also prove the sparse characteristic of feature. Our feature extraction model is based on the fact that in sparse coding the energy of the signal is concentrated on a few components only, while the energy of additive noise remains uniformly spreading on all the components.