Explore MATLAB source code curated for "时频分析" with clean implementations, documentation, and examples.
This repository contains complete MATLAB code for general processing of non-stationary signals in time-frequency analysis, featuring signal preprocessing, spectral analysis algorithms, and visualization techniques.
Time-frequency analysis of linear frequency modulated signals using Short-Time Fourier Transform with implementation insights for signal processing applications
MATLAB source code implementations for fundamental communication algorithms including phase space analysis, wavelet frames, and time-frequency analysis techniques
Time-frequency analysis techniques for measuring and estimating parameters of non-stationary signals, with implementation approaches using signal processing algorithms.
Application Background: Empirical Mode Decomposition (EMD) is a time-frequency analysis method for processing nonlinear and non-stationary signals. This method adaptively decomposes input signals into several Intrinsic Mode Functions (IMFs) based on their inherent characteristics without requiring prior knowledge. It is widely used in signal denoising and non-stationary time series prediction. Key Technology: The EMD algorithm enables denoising, analysis, and prediction of high-frequency signals through decomposition and trend analysis. The MATLAB implementation typically involves iterative sifting processes, envelope detection using cubic spline interpolation, and stopping criteria based on standard deviation thresholds.
Utilizing four-component Gaussian envelope signals for time-frequency analysis, comparing resolution and cross-term interference among STFT, WVD, and CWD methods. Includes simulation code and MATLAB time-frequency analysis toolbox with implementation notes. Toolbox usage recommendations reference "MATLAB Time-Frequency Analysis Technology and Its Applications - Ge Zhexue", with the book's source code included in the package for practical integration.
Fourier Transform and Time-Frequency Analysis of Three Piecewise-Connected Signals; Fourier Transform and Time-Frequency Analysis of Chirp Signals; Subband Decomposition of Signals with Implementation Approaches
Time-frequency analysis of non-stationary signals using Short-Time Fourier Transform (STFT) with implementation insights for signal processing applications
Implementation routine and practical applications of S-Transform for time-frequency analysis of non-stationary signals, featuring Python code examples and signal processing methodology.
This is a time-frequency analysis program implementing Gabor transform, which demonstrates excellent frequency separation capabilities. The Gabor transform parameters include constants a and b, where a represents the grid interval length and b represents the grid frequency length. The expansion coefficients correspond to one-dimensional signal x(t), with h(t) as the mother function generating basis functions through shifting and modulation operations.