Fast Hopping Discrete Sine Transform

Discrete sine transform (DST) is widely used in digital signal processing such as image coding, spectral analysis, feature extraction, and filtering. This is because the discrete sine transform is close to the optimal Karhunen-Loeve transform for first-order Markov stationary signals with low correl...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 94293 - 94298
Main Author: Kober, Vitaly
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biomedical data
Computing costs
Correlation coefficient
Correlation coefficients
Digital imaging
Digital signal processing
Discrete cosine transforms
Discrete sine transform
Feature extraction
hopping algorithm
Image coding
Laplace equations
short-time transform
Signal processing
Signal processing algorithms
sliding algorithm
Spectral analysis
Spectrum analysis
Time-frequency analysis
Transforms
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Discrete sine transform (DST) is widely used in digital signal processing such as image coding, spectral analysis, feature extraction, and filtering. This is because the discrete sine transform is close to the optimal Karhunen-Loeve transform for first-order Markov stationary signals with low correlation coefficients. Short-time (hopping) discrete sine transform can be employed for time-frequency analysis and adaptive processing quasi-stationary data such as speech, biomedical, radar and communication signals. Hopping transform refers to a transform computed on the signal of a fixed-size window that slides over the signal with an integer hop step. In this paper, we first derive a second-order recursive equation between DST spectra in equidistant signal windows, and then propose two fast algorithms for computing the hopping DST based on the recursive relationship and input-pruned DST algorithm. The performance of the proposed algorithms with respect to computational costs and execution time is compared with that of conventional sliding and fast DST algorithms. The computational complexity of the developed algorithms is lower than any of the existing algorithms, resulting in significant time savings.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3094277