Multi-label algorithm based on rough set of fractal dimension attribute
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To make fractal endpoint detection algorithm, to maintain good performance and to deal with noise with higher irregularity than speech, fractal endpoint detection algorithm based on frequency domain was proposed in the paper. The frequency domain represented energy distribution and signal, and the speech harmonic component had very strong periodicity and regularity in the frequency domain. Thus, method of extracting fractal dimension after converting to short-time frequency domain had better robustness. Analysis means were introduced based on short-time frequency domain fractal against the existing fractal algorithm. Its stability was due to the frequency domain represented frequency domain energy distribution of signal and the speech signal energy mainly focused on harmonic. Thus, solving fractal dimension in short-time frequency domain could weaken the impact of different types of noises. Compared with time-domain fractal, the threshold value of short-time frequency domain fractal was more stable and the judgment criterion direction was fixed, smaller than the represented speech fragment of threshold value. Frequency domain was used for representing the signal energy distribution characteristics and the strong periodicity and regularity of speech harmonic component so as to extract fractal dimension and distinguish speech and noise. Thus, the fractal dimension extraction method after converting to short-time frequency domain proposed in the paper had better robustness. Not only is it applicable to irregular white noise, but also applicable to noises with stronger time-domain periodicity and regularity including tank noise.
KeywordsFractal algorithm Rough set Label Short-time frequency domain Harmonic component
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