Randomized Continuous Frames in Time-Frequency Analysis
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Recently, a Monte Carlo approach was proposed for speeding up signal processing pipelines based on continuous frames. In this paper we present and analyze applications of this new theory. The computational complexity of the Monte Carlo method relies on the continuous frame being so called linear volume discretizable (LVD). The LVD property means that the number of samples in the coefficient space required by the Monte Carlo method is proportional to the resolution of the discrete signal. We show in this paper that the continuous wavelet transform (CWT) and the localizing time-frequency transform (LTFT) are LVD. The LTFT is a time-frequency representation based on a 3D time-frequency space with a richer class of time-frequency atoms than classical time-frequency transforms like the short time Fourier transform (STFT) and the CWT. Our analysis proves that performing signal processing with the LTFT is as efficient as signal processing with the STFT and CWT (based on FFT), even though the coefficient space of the LTFT is higher dimensional.
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