Signal processing with fractals: a wavelet-based approach
Signal processing with fractals: a wavelet-based approach
Fractal Speech Processing
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
Chaos and Fractals
Signal classification through multifractal analysis and complex domain neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper describes how the selection of parameters for the variance fractal dimension VFD multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a natural-speech waveform in the presence of ambient noise. The technique is based on the variance fractal dimension trajectory VFDT algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The VFDT algorithm can also amplify internal features of phonemes. This fractal feature amplification is accomplished when the time increments are selected in a dyadic manner rather than selecting the increments in a unit distance sequence. These amplified trajectories for different phonemes are more distinct, thus providing a better characterization of the individual segments in the speech signal. This approach is superior to other energy-based boundary-detection techniques. Observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.