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- W2080771415 abstract "Detecting and identifying non-stationary signals is well known to be important in many sonar applications. While great strides have been made in the last 60 years of underwater sonar, every effort is still needed to detect and then identify whatever glimpses in the ocean can be found. The traditional method for analyzing such signals, which contain time-varying frequencies, is the short-time Fourier transform (STFT) or lofargram. However, neither the STFT nor other methods, such as wavelet transforms and Wigner-Ville distributions, that have found favor in speech processing, are always well-suited to non-stationary signals. The story remains the same out of the water too. The fractional Fourier transform (FrFT) has emerged in recent years as a powerful tool for analyzing complicated non-stationary signals. As an example, we first apply the FrFT, alongside other time-frequency methods, to examine real bat chirps, marine mammal soundings, and speech signals. A new short-time FrFT, which accounts for signals with multiple non-stationary components, is then demonstrated, allowing us the flexibility to track several different signals at the same time. The remainder of the paper outlines some of the classification techniques, which complement the output from the short-time FrFT, trying to identify non-stationary signals of every kind." @default.
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- W2080771415 date "2007-05-01" @default.
- W2080771415 modified "2023-09-25" @default.
- W2080771415 title "Detecting and identifying non‐stationary signals" @default.
- W2080771415 doi "https://doi.org/10.1121/1.4808523" @default.
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