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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2003 / Cilt: 3 - Sayı: 2

Least squares multi-window evolutionary spectral estimation

Aydın AKAN
Mahmut YALÇIN
Pages
899–903
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Examples are given to illustrate the effectiveness of the proposed method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We present a multi-window method for obtaining the time-frequency spectrum of non-stationary signals such as speech and music.
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Key Findings & Data:This method is based on optimal combination of evolutionary spectra that are calculated by using multi-window Gabor expansion.
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Conclusion & Impact:Examples are given to illustrate the effectiveness of the proposed method.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

We present a multi-window method for obtaining the time-frequency spectrum of non-stationary signals such as speech and music. This method is based on optimal combination of evolutionary spectra that are calculated by using multi-window Gabor expansion. The optimal weights are obtained by using a least square estimation method. An error criterion that is the squared distance between a reference time-frequency distribution and the combination of evolutionary spectra is minimized to determine the weights. Examples are given to illustrate the effectiveness of the proposed method.

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Cite This Article

AKAN, A., YALÇIN, M. (2022). Least squares multi-window evolutionary spectral estimation. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 3 - Sayı: 2, 899–903. https://ajindex.com/articles/543905

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