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The research on stochastic resonance phenomenon of neuron had shown the important theoretical significance and application value of the weak signal detection. The robustness performed not very well during the process of the weak signal detection, which based on the stochastic resonance of the traditional FitzHugh-Nagumo (FHN) neuron model. The addition of feedback loop which achieved the reaction...
The aim of this work is to propose an automatic sleep stage classification technique of electroencephalogram's signals (EEG) using Hilbert-Huang transform. EEG signals are analyzed with the Hilbert-Huang transform, instantaneous frequency with the physical meaning is obtained; The energy-frequency distribution of EEG was used as features parameters for each sleep stage; Ultimately using nearest neighbor...
Nowadays, noise was considered to play a key role in the neural system, which improved the weak signal detection and enhanced the information transmission, as deeply research on stochastic resonance (SR). Traditionally, stochastic resonance was thought to be the result of the interaction between subthreshold signals with noise in nonlinear system. Particularly, the existence of noise was thought to...
Automatic speaker gender identification based on the speech feature has important application in the audio processing and analysis field. In order to overcome the conventional linear parameters in the speaker feature lack of gender characteristics, in this paper, nonlinear parameters such as the fractal dimension and fractal complexity as feature space effective compensations are presented. Firstly,...
Brain-computer interface research focused on using electroencephalogram(EEG) from the scalp over sensorimotor cortex to control outer device. The studies seek to improve the classification accuracy by improving the selection of signal features based on non-linear methods. Since EEG signals may be considered chaotic, chaos theory may supply effective quantitative descriptors of EEG dynamics and of...
Texture image segmentation consists of two stages: feature extraction and classification. The new method advanced in this paper fuses the log-gabor filter and DCT features in the first stage, then uses the fusion of fuzzy c-means (FCM) and support vector machines (SVM) classifier to cluster the fused feature sets. The fused feature sets produce higher feature space separations, and the fusion of multi-classifiers...
Accurate endpoint detection is crucial for good speech recognition accuracy. A new algorithm based on C0 complexity measure is proposed in this paper. In the comparison of the new algorithm to the other traditional methods such as energy, ZCR, spectral-entropy etc., for the continuous speech and isolated word speech, the C0 complexity feature proved effective. The proposed algorithm is shown to be...
Texture, a representation of the spatial relationship of gray levels in an image, is an important characteristic for the automated or semi-automated interpretation of digital images. Many previous analyses have shown how to discriminate texture images, which include gray level co-occurrence matrix (GLCM), Laws' texture energy (LAWS) and Gabor multi-channel filtering (GABOR) etc. We have devised a...
Electroencephalograms (EEGs) reflect the electrical activity of the brain. The problem of analyzing and interpreting the meaning of these signals has received a great deal of attention. Since EEG signals may be considered chaotic, chaos theory may supply effective quantitative descriptors of EEG dynamics and of underlying chaos in the brain. The complexity of the chaotic system can be characterized...
In the process of speech recognition, it is especially crucial to precisely locate endpoints of the input utterance to be free of non-speech regions. This paper proposes a novel approach that finds robust features for endpoint detection in a noisy environment. In this proposed method, we integrate both time-frequency enhancement and the spectral entropy feature. Firstly, the noisy speech is enhanced...
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