Fast discrete orthonormal Stockwell transform. We present an efficient method for computing the discrete orthonormal Stockwell transform (DOST). The Stockwell transform (ST) is a time-frequency decomposition transform that is showing great promise in various applications, but is limited because its computation is infeasible for most applications. The DOST is a nonredundant version of the ST, solving many of the memory and computational issues. However, computing the DOST of a signal of length N using basis vectors is still 𝒪(N 2 ). The computational complexity of our method is 𝒪(NlogN), putting it in the same category as the FFT. The algorithm is based on a simple decomposition of the DOST matrix. We also explore the way to gain conjugate symmetry for the DOST and propose a variation of the parameters that exhibits symmetry, akin to the conjugate symmetry of the FFT of a real-valued signal. Our fast method works equally well on this symmetric DOST. In this paper, we provide a mathematical proof of our results and derive that the computational complexity of our algorithm is 𝒪(NlogN). Timing tests also confirm that the new method is orders-of-magnitude faster than the brute-force DOST, and they demonstrate that our fast DOST is indeed 𝒪(NlogN) in complexity.
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References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Hleili, Khaled; Hleili, Manel: Time-frequency analysis of localization operators for the non-isotropic $n$-dimensional modified Stockwell transform (2021)
- Shah, Firdous A.; Tantary, Azhar Y.: Non-isotropic angular Stockwell transform and the associated uncertainty principles (2021)
- Singh, Neha; Pradhan, Pyari Mohan: S-transform based on optimally concentrated time-limited and band-limited windows (2020)
- Battisti, Ubertino; Berra, Michele; Tabacco, Anita: Stockwell-like frames for Sobolev spaces (2018)
- Singh, Pushpendra: Breaking the limits: redefining the instantaneous frequency (2018)
- Yan, Yusong; Zhu, Hongmei: Visualizing discrete complex-valued time-frequency representations (2017)
- Battisti, U.; Riba, L.: Window-dependent bases for efficient representations of the Stockwell transform (2016)
- Riba, L.; Wong, M. W.: Continuous inversion formulas for multi-dimensional modified Stockwell transforms (2015)
- Wang, Yanwei; Orchard, Jeff: Fast discrete orthonormal Stockwell transform (2009)