Topic
Spectral density estimation
About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.
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Journal Article•
TL;DR: The inductively coupled plasma atomic emission spectrometry (ICP-AES) and its signal characteristics were discussed and the spectral estimation technique was helpful for the better understanding about spectral composition and signal characteristics.
Abstract: The inductively coupled plasma atomic emission, spectrometry (ICP-AES) and its signal characteristics were discussed using modem spectral estimation technique The power spectra density (PSD) was calculated using the auto-regression (AR) model of modem spectra estimation The Levinson-Durbin recursion method was used to estimate the model parameters which were used for the PSD computation The results obtained with actual ICP-AES spectra and measurements showed that the spectral estimation technique was helpful for the better understanding about spectral composition and signal characteristics
34 citations
TL;DR: The accuracy of spectral estimation with this system and each estimation technique is evaluated and the system's performance is presented.
Abstract: In this paper, the analysis methods used for developing imaging systems estimating spectral reflectance are considered. The chosen system incorporates an estimation technique for spectral reflectance. Several traditional and machine learning estimation techniques are compared for this purpose. The accuracy of spectral estimation with this system and each estimation technique is evaluated and the system's performance is presented.
34 citations
TL;DR: It is shown that one of the best substitutions for the Gaussian function in the Fourier domain is a squared sinusoid function that can form a biorthogonal windowfunction in the time domain.
Abstract: We discuss the semicontinuous short-time Fourier transform (STFT) and the semicontinual wavelet transform (WT) with Fourier-domain processing, which is suitable for optical implementation. We also systematically analyze the selection of the window functions, especially those based on the biorthogonality and the orthogonality constraints for perfect signal reconstruction. We show that one of the best substitutions for the Gaussian function in the Fourier domain is a squared sinusoid function that can form a biorthogonal window function in the time domain. The merit of a biorthogonal window is that it could simplify the inverse STFT and the inverse WT. A couple of optical architectures based on Fourier-domain processing for the STFT and the WT, by which real-time signal processing can be realized, are proposed.
34 citations
TL;DR: A frequency-adaptive robust technique for the accurate estimation of the single-phase grid voltage fundamental and harmonic parameters, based on the discrete Fourier transform and a cascaded delayed signal cancellation strategy is reported.
Abstract: This paper reports a frequency-adaptive robust technique for the accurate estimation of the single-phase grid voltage fundamental and harmonic parameters. The technique is based on the discrete Fourier transform and a cascaded delayed signal cancellation strategy. There is no stability issue in the technique, since it does not contain any type of feedback loop. It can also be flexibly configured to estimate the parameters of the fundamental and/or one/multiple harmonic(s) from the grid voltage waveform distorted by various harmonics. Moreover, it does not require evaluation of trigonometric and inverse trigonometric functions for implementing on real-time digital signal processor. However, it needs computationally demanding high-order finite-impulse-response filters. The simulation and real-time experimental results are provided to verify the performance of the proposed technique.
34 citations
Posted Content•
TL;DR: This work proposes PercepNet, an efficient approach that relies on human perception of speech by focusing on the spectral envelope and on the periodicity of the speech, and demonstrates high-quality, real-time enhancement of fullband speech with less than 5% of a CPU core.
Abstract: Over the past few years, speech enhancement methods based on deep learning have greatly surpassed traditional methods based on spectral subtraction and spectral estimation. Many of these new techniques operate directly in the the short-time Fourier transform (STFT) domain, resulting in a high computational complexity. In this work, we propose PercepNet, an efficient approach that relies on human perception of speech by focusing on the spectral envelope and on the periodicity of the speech. We demonstrate high-quality, real-time enhancement of fullband (48 kHz) speech with less than 5% of a CPU core.
34 citations