Explore MATLAB source code curated for "频谱分析" with clean implementations, documentation, and examples.
Wavelet packet decomposition for reconstructing bearing vibration signals, combined with Hilbert envelope analysis and FFT spectral analysis to extract bearing fault frequencies. Includes code implementation details for signal processing workflow.
1. Generate a continuous signal containing low, medium, and high frequency components, perform sampling and spectral analysis. Design three types of filters (high-pass, low-pass, and band-pass) to process the signal and observe the spectrum of filtered signals. 2. Acquire a noisy speech signal (either by recording with background noise or adding noise to clean speech), perform sampling and spectral analysis, then design an appropriate filter to eliminate noise based on spectral characteristics.
Self-developed FFT and DTFT programs for spectrum analysis, incorporating the Rife spectrum correction technique. Includes MATLAB implementations of these algorithms with graphical outputs. All code has been debugged and verified for proper execution.
This tutorial demonstrates how to perform spectrum analysis using FFT with a practical MATLAB example. The signal_analyzer.rar package contains test data (test1.txt) with time-series input/output signals and a MATLAB script (signal_analyzer.m) that performs FFT analysis to calculate frequency characteristics, amplitude attenuation in dB, and phase differences between signals.
MATLAB-based time-domain signal sampling and spectral analysis project providing practical source code for understanding signal processing fundamentals through hands-on implementation examples.
Implementation of a 50Hz power frequency notch filter based on Butterworth design, featuring filter coefficient calculation and comprehensive spectral analysis with MATLAB code examples
The S-transform is a widely used time-frequency analysis algorithm particularly effective for seismic data analysis, enabling both time-frequency decomposition and spectral examination. This MATLAB-based implementation provides complete forward and inverse S-transform functionality with optimized computational efficiency.
Implementation of Discrete Fourier Transform (DFT) and Spectral Analysis using MATLAB. Signal function: h(t) = e^(-a*t)*sin(2πft). Program includes DFT/IDFT visualization, amplitude spectrum calculation, phase spectrum analysis, and main frequency detection with code implementation details.
Frequency Step Signal Ambiguity Function with Ambiguity Diagram Simulation, Time Domain Waveform Analysis, and Spectral Analysis
A self-developed signal generation and processing software built on MATLAB 7 platform. It implements deterministic and stochastic signal generation algorithms, various digital filter design methods (FIR/IIR), spectral analysis using FFT algorithms, and post-filtering frequency domain analysis. This tool provides practical implementation examples for Signals and Systems and Digital Signal Processing courses, featuring GUI-based parameter configuration and real-time visualization of signal waveforms and spectra.