Page 53 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

0.8 1 1 1
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Audio Signal Detail Detail Detail
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Detail Detail Detail Detail
(e) (f) (g) (h)
Fig. 11 – Approximation and detail coefficients obtained from an audio signal by some well-known wavelet transforms and MSICA.
(a) Original audio Signal. Approximation and detail by (b) Daubechies 3; (c) Haar; (d) Biorthogonal 2.2; (e) Coiflets 4; (f) Fejer-Korovkin 4;
(g) discrete Meyer; (h) MSICA.
have examined its performance on the signal depicted 2011.
in Fig. 11(a), which is an audio signal with a sampling [2] Y. Kopsinis and S. McLaughlin, “Development of emd-
frequency equal to 16 KHz. Fig. 11(b)-(g) show the based denoising methods inspired by wavelet threshold-
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We presented MSICA, a new method for Multi-Scale Processing, vol. 162, pp. 10–20, 2019.
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tistically independent. First, we extracted two corre- profile for the classification of hyperspectral images,”
IEEE Journal of Selected Topics in Applied Earth Ob-
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signals leads to the decomposition of the original sig- scale space for image restoration,” IEEE Transactions
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MSICA outperforms well-known wavelet transforms in 2010.
signal denoising when transmitting a signal over mul- [7] G. Gilboa, “Nonlinear scale space with spatially varying
tiple (noisy) channels as it exploits channel diversity. stopping time,” IEEE Transactions on Pattern Analysis
This property makes MSICA useful in many critical and Machine Intelligence, vol. 30, no. 12, pp. 2175–2187,
scenarios such as transmitting multimedia content reli- 2008.
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