Multiple Window Time-Varying Spectrum Estimation
نویسندگان
چکیده
We overview a new non-parametric method for estimating the time-varying spectrum of a non-stationary random process. Our method extends Thomson’s powerful multiple window spectrum estimation scheme to the time-frequency and time-scale planes. Unlike previous extensions of Thomson’s method, we identify and utilize optimally concentrated Hermite window and Morse wavelet functions and develop a statistical test for extracting chirping line components. Examples on synthetic and real-world data illustrate the superior performance of the technique.
منابع مشابه
Multiple Window Time - Frequency
We propose a robust method for estimating the time-varying spectrum of a non-stationary random process. Our approach extends Thomson's powerful multiple window spectrum estimation scheme to the time-frequency and timescale planes. The method reenes previous extensions of Thomson's method through optimally concentrated window and wavelet functions and a statistical test for extracting chirping l...
متن کاملMultiple Window Time-Frequency and Time-Scale Analysis
We propose an extension of Thomson's multiple window spectrum estimation for stationary random processes to the time-varying spectrum estimation of non-stationary random processes. Unlike previous extensions of Thomson's method, in this paper we identify and utilize optimally concentrated window and wavelet functions for the time-frequency and timescale planes respectively. Moreover, we develop...
متن کاملMultiple window non-linear time-varying spectral analysis
A non-linear multi-window method for generating a time-varying spectrum of non-stationary signals in noise is presented. The timevarying spectrum is computed from an optimally weighted average of multiple Hermite windowed spectrograms. The weights are determined using linear least squares estimation with respect to a reference time-frequency distribution. A masking operation is also used to red...
متن کاملMultiple WindowTime - Varying Spectrum
We overview a new non-parametric method for estimating the time-varying spectrum of a non-stationary random process. Our method extends Thomson's powerful multiple window spectrum estimation scheme to the time-frequency and timescale planes. Unlike previous extensions of Thomson's method, we identify and utilize optimally concentrated Hermite window and Morse wavelet functions and develop a sta...
متن کاملGroup Sparsity Based Spectrum Estimation of Harmonic Speech Signals
Spectrum analysis of speech signals is important for their detection, recognition, and separation. Speech signals are nonstationary with time-varying frequencies which, when analyzed by Fourier analysis over a short time window, exhibit harmonic spectra, i.e., the fundamental frequencies are accompanied by multiple associated harmonic frequencies. With proper modeling, such harmonic signal comp...
متن کامل