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An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization
Joint Authors
Nisar, Shibli
Khan, Omar Usman
Tariq, Muhammad
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-24
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal.
The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution.
The selection of an appropriate window size is difficult when no background information about the input signal is known.
In this paper, a novel empirical model is proposed that adaptively adjusts the window size for a narrow band-signal using spectrum sensing technique.
For wide-band signals, where a fixed time-frequency resolution is undesirable, the approach adapts the constant Q transform (CQT).
Unlike the STFT, the CQT provides a varying time-frequency resolution.
This results in a high spectral resolution at low frequencies and high temporal resolution at high frequencies.
In this paper, a simple but effective switching framework is provided between both STFT and CQT.
The proposed method also allows for the dynamic construction of a filter bank according to user-defined parameters.
This helps in reducing redundant entries in the filter bank.
Results obtained from the proposed method not only improve the spectrogram visualization but also reduce the computation cost and achieves 87.71% of the appropriate window length selection.
American Psychological Association (APA)
Nisar, Shibli& Khan, Omar Usman& Tariq, Muhammad. 2016. An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099722
Modern Language Association (MLA)
Nisar, Shibli…[et al.]. An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1099722
American Medical Association (AMA)
Nisar, Shibli& Khan, Omar Usman& Tariq, Muhammad. An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099722
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099722