OALib Journal期刊
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Time-oscillating Lyapunov modes and auto-correlation functions for quasi-one-dimensional systems
The time-dependent structure of the Lyapunov vectors corresponding to the steps of Lyapunov spectra and their basis set representation are discussed for a quasi-one-dimensional many-hard-disk systems. Time-oscillating behavior is observed in two types of Lyapunov modes, one associated with the time translational invariance and another with the spatial translational invariance, and their phase relation is specified. It is shown that the longest period of the Lyapunov modes is twice as long as the period of the longitudinal momentum auto-correlation function. A simple explanation for this relation is proposed. This result gives the first quantitative connection between the Lyapunov modes and an experimentally accessible quantity.
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Live SupportAsk us anythingFeature extraction from higher-lag autocorrelation coefficients for robust speech recognition - ScienceDirect
Export JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page., November 2006, Pages Author links open overlay panelShow moreAbstractIn this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower-time lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses only the higher-lag autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-lag autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further (like the well-known Mel frequency cepstral coefficient (MFCC) procedure) by the Mel filter bank, log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the autocorrelation Mel frequency cepstral coefficients (AMFCCs). We evaluate the speech recognition performance of the AMFCC features on the Aurora and the resource management databases and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech. Finally, we show that the AMFCC features perform better than the features derived from the robust linear prediction-based methods for noisy speech.KeywordsSpeech recognitionFeature extractionRobustness to noiseMFCCCheck if you have access through your login credentials or your institution.ororRecommended articlesCiting articles (0)}