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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 6172453, 13 pages
Research Article

An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization

1National University of Computer and Emerging Sciences, Peshawar 25000, Pakistan
2Princeton University, New Jersey, NJ 08544, USA

Received 30 March 2016; Revised 24 June 2016; Accepted 13 July 2016

Academic Editor: Silvia Conforto

Copyright © 2016 Shibli Nisar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.