Randomized continuous frames in time-frequency analysis

Ron Levie*, Haim Avron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Recently, a Monte Carlo approach was proposed for processing highly redundant 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 has the same asymptotic complexity as signal processing with the STFT and CWT (based on FFT), even though the coefficient space of the LTFT is higher dimensional.

Original languageEnglish
Article number25
JournalAdvances in Computational Mathematics
Issue number3
StatePublished - Jun 2022


FundersFunder number
United States-Israel Binational Science Foundation2017698


    • Continuous wavelet transform
    • Localizing time-frequency transform
    • Phase vocoder
    • Signal processing
    • Stochastic methods
    • Time-frequency analysis


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