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.

Zero-crossing based spectral analysis and SVD spectral analysis for formant frequency estimation in noise

Abstract: The authors discuss a method for spectral analysis of noise corrupted signals using statistical properties of the zero-crossing intervals. It is shown that an initial stage of filter-bank analysis is effective for achieving noise robustness. The technique is compared with currently popular spectral analysis techniques based on singular value decomposition and is found to provide generally better resolution and lower variance at low signal to noise ratios (SNRs). These techniques, along with three established methods and three variations of these method, are further evaluated for their effectiveness for formant frequency estimation of noise corrupted speech. The theoretical results predict and experimental results confirm that the zero-crossing method performs well for estimating low frequencies and hence for first formant frequency estimation in speech at high noise levels ( approximately 0 dB SNR). Otherwise, J.A. Cadzow's high performance method (1983) is found to be a close alternative for reliable spectral estimation. As expected the overall performance of all techniques is found to degrade for speech data. The standard autocorrelation-LPC method is found best for clean speech and all methods deteriorate roughly equally in noise. >

Bayesian spectrum estimation of unevenly sampled nonstationary data

Abstract: Spectral estimation methods typically assume stationarity and uniform spacing between samples of data. The non-stationarity of real data is usually accommodated by windowing methods, while the lack of uniformly-spaced samples is typically addressed by methods that “fill in” the data in some way. This paper presents a new approach to both of these problems: We use a non-stationary Kalman filter within a Bayesian framework to jointly estimate all spectral coefficients instantaneously. The new method works regardless of how the signal samples are spaced. We illustrate the method on several data sets, showing that it provides more accurate estimation than the Lomb-Scargle method and several classical spectral estimation methods.

Locally adaptive lag‐window spectral estimation

Abstract: . We propose a procedure for the locally optimal window width in nonparametric spectral estimation, minimizing the asymptotic mean square error at a fixed frequency Λ of a lag-window estimator. Our approach is based on an iterative plug-in scheme. Besides the estimation of a spectral density at a fixed frequency, e.g. at frequency Λ= 0, our procedure allows to perform nonparametric spectral estimation with variable window width which adapts to the smoothness of the true underlying density.