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- W2401001075 abstract "Various transformations have been widely used to extract frequency-dependent information from time-varying signals. The Fourier transformation is a fundamental technique for transforming a time domain to a frequency domain, but is applicable only to stationary signals. Sliding-window-based transformations have been used, where the requirement for time and frequency resolutions is not critical. Due to the uncertainty principle, time and frequency resolutions have to be optimized. In such cases, wavelet transformations are very popular to get salient features in time-dependent signals. Multiresolution analysis and selection of mother functions are also discussed. Some relevant examples from electroencephalograms and speech signals are used to illustrate the significance of these transformations in time-frequency analysis." @default.
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- W2401001075 date "2016-01-01" @default.
- W2401001075 modified "2023-09-26" @default.
- W2401001075 title "Time and Frequency Analysis" @default.
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- W2401001075 doi "https://doi.org/10.1016/b978-0-12-804490-2.00004-x" @default.
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