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.
Papers published on a yearly basis
Papers
More filters
TL;DR: A method of determining the periodicities of gastrointestinal data using an autoregressive modelling technique is presented and is both automatic and capable of yielding frequency estimates on as little as 5 cycles of data corrupted with noise.
Abstract: A method of determining the periodicities of gastrointestinal data using an autoregressive modelling technique is presented. The method has been applied both to simulated sinusoidal data and to gastrointestinal and colonic data. Unlike the fast Fourier transforms, the method presented is both automatic and capable of yielding frequency estimates on as little as 5 cycles of data corrupted with noise.
41 citations
25 Oct 2020
TL;DR: PercepNet as mentioned in this paper 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 (48 kHz) 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.
41 citations
TL;DR: The cone-shaped kernel generalized time-frequency representation (GTFR) of Zhao, Atlas, and Marks has been shown empirically to generate quite good time frequency representation in comparison to other approaches.
Abstract: The cone-shaped kernel generalized time-frequency representation (GTFR) of Zhao, Atlas, and Marks (ZAM) has been shown empirically to generate quite good time frequency representation in comparison to other approaches. The authors analyze some specific properties of this GTFR and compare them to other TFRs. Asymptotically, the GTFR is shown to produce results identical to that of the spectrogram for stationary signals. Interference terms normally present in many GTFRs are shown to be attenuated drastically by the use of the ZAM-GTFR. The ability of the ZAM-GTFR to track frequency hopping is shown to be close to that of the Wigner distribution. When a signal is subjected to white noise, the ZAM-GTFR produces an unbiased estimate of the ZAM-GTFR of the signal without noise. In many other GTFRs, the power spectral density of the noise is superimposed on the GTFR of the signal. It is also shown that, in discrete form, the ZAM-GTFR is generally invertible. >
41 citations
01 Dec 1992
TL;DR: In this paper, the authors describe how windows modify the magnitude of a discrete Fourier transform and the level of a power spectral density computed by Welch's method, and show that the signal-to-noise ratio in a single discrete transform is related to the normal time-domain definition of the signal to noise ratio.
Abstract: The author describes how windows modify the magnitude of a discrete Fourier transform and the level of a power spectral density computed by Welch's method. For white noise, the magnitude of the discrete Fourier transform at a fixed frequency has a Rayleigh probability distribution. For sine waves with an integer number of cycles and quantization noise, the theoretical values of the amplitude of the discrete Fourier transform and power spectral density are calculated. The authors show the signal-to-noise ratio in a single discrete Fourier transform or power spectral density frequency bin is related to the normal time-domain definition of the signal-to-noise ratio. The answer depends on the discrete Fourier transform length, the window type, and the function averaged. >
41 citations
TL;DR: In this article, weakly and strongly consistent nonparametric estimates, along with rates of convergence, are established for the spectral density of certain stationary stable processes, which plays a role, in linear inference problems, analogous to that played by the usual power spectral densities of second order stationary processes.
41 citations