Classic MATLAB Time-Frequency Analysis: Code Implementation and Applications

This article explores classic MATLAB time-frequency analysis code, a widely-used tool for analyzing various signals including audio, sound, and electronic signals. Time-frequency analysis represents a signal processing technique that examines how signal components evolve across both time and frequency domains, making it applicable across multiple fields such as music, communications, medical imaging, and industrial control systems. MATLAB provides powerful built-in functions and toolboxes that facilitate efficient implementation of time-frequency analysis algorithms, enabling seamless data visualization and analysis.

The implementation typically involves MATLAB's Signal Processing Toolbox functions like spectrogram() for Short-Time Fourier Transform (STFT) analysis, which computes the Fourier transform of windowed signal segments over time. For higher resolution analysis, wavelet transforms can be implemented using cwt() for continuous wavelet transform or dwt() for discrete wavelet transform. The code generally follows this structure: signal preprocessing, parameter configuration (window size, overlap, wavelet type), transformation computation, and result visualization using imagesc() or contour plots.

We will demonstrate fundamental time-frequency analysis principles in MATLAB and provide practical code examples for analysis and visualization. The article will also cover common applications and case studies, including speech signal analysis using spectrograms, vibration signal monitoring with wavelet transforms, and biomedical signal processing applications. These examples will help readers better understand the practical implementation and significance of time-frequency analysis in real-world scenarios.