We present a complexity measure for any finite time series. This measure has invariance under any monotonic transformation of the time series, has a degree of robustness against noise, and has the adaptability of satisfying almost all the widely accepted but conflicting criteria for complexity measurements. Surprisingly, the measure is developed from Kolmogorov complexity, which is traditionally believed to represent only randomness and to satisfy one criterion to the exclusion of the others. For familiar iterative systems, our treatment may imply a heuristic approach to transforming symbolic dynamics into permutation dynamics and vice versa.
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 energyfrequency distribution of EEG was used as features parameters for each sleep stage; Ultimately using nearest neighbor method for pattern classification complete classifying sleep stage. According to experimental results of 560 samples of sleep EEG, average accuracy rate of the method achieved 81.7%. In a word, The EEG Hilbert-Huang transform based method can be used as an effective sleep staging classification.
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, use lifting scheme to extract pitch; Then extract the speech fractal dimension; Finally, according Takens theorem, time delay method is used to reconstruct phase space of fractal dimension sequence, fractal dimension complexity is obtained by calculating Approximate Entropy. Three dimension feature vectors constructed by the pitch, the fractal dimension and the fractal dimension complexity are applied to speaker gender identification. Results show as the identification system based on the new method introduces the non-linear parameters, its accuracy and stability are effectively improved compared with the traditional linear method identification systems. The new nonlinear method provides new ideas for speaker gender identification of a new line of thought.
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