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 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 technology; mathematical modeling of neural platforms; balance and nonlinear research; feedforward and fuzzy neural networks; probabilistic equipment; supervised studying; unsupervised studying; aid vector laptop and kernel tools; hybrid optimisation algorithms; laptop studying and information mining; clever regulate and robotics; development acceptance; audio picture processinc and desktop imaginative and prescient; fault analysis; functions and implementations; functions of neural networks in digital engineering; mobile neural networks and complicated regulate with neural networks; nature encouraged equipment of high-dimensional discrete info research; development popularity and data processing utilizing neural networks.
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Additional resources for Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, September 24-28, 2008, Proceedings, Part I
Neurosci. 2, 252–263 (2002) 10. : Cognitive and biological determinants of P300: an integrative review. Biol. Psychol. 41, 103–146 (1995) 11. : Increased Event-related Potential Latency and Amplitude Variability in Schizophrenia Detected through Waveletbased Single Trial Analysis. Int. J. Psychophysiology 66, 244–254 (2007) 12. : Blind noise reduction for multisensory signals using ICA and subspace filtering with application to EEG analysis. Biol. Cybern. 86, 293–303 (2002) 13. : Application of adaptive noise cancellation with neuralnetwork-based fuzzy inference system for visual evoked potentials estimation.
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 .
Alternating projection algorithm is applied to obtain a stable solution. Experiments results demonstrate that our method can improve the recognition accuracy specifically in noise environment. 1 Introduction Speaker recognition is the task of determining the identification of a person from one’s voice which has great potential applications in industry, business and security, etc. For a speaker recognition system, feature extraction is one of important tasks, which aims at finding succinct, robust, and discriminative features from acoustic data.