MATLAB Code Implementation for STFT Spectral Decomposition

STFT (Short-Time Fourier Transform) is a signal processing method widely used in time-frequency analysis. It enables intuitive visualization of how signal frequency spectra evolve over time. Implementing STFT spectral decomposition in MATLAB typically involves the following key steps: Signal Preprocessing First, perform necessary preprocessing on the input signal, such as removing DC components, normalization, or windowing. Common window functions include Hanning and Hamming windows to reduce spectral leakage effects. In MATLAB, this can be implemented using functions like `detrend` for DC removal and `hann`/`hamming` for window generation. Frame Segmentation and Windowing Divide the signal into short-time frames, typically with durations ranging from tens to hundreds of milliseconds. Frames can overlap with each other. Apply window functions to each frame to minimize boundary effects. The `buffer` function can be utilized for frame segmentation with overlap control. FFT Transformation Perform Fast Fourier Transform (FFT) on each frame to compute its frequency spectrum. MATLAB's `fft` function efficiently implements this step, where the FFT length can be optimized using parameters like `nfft` for zero-padding. Spectral Calculation Compute the magnitude or power spectrum for each frame and combine them into a time-frequency matrix. Logarithmic scales (such as dB) are commonly used to enhance visual contrast. The calculation typically involves `abs()` for magnitude and `10*log10()` for dB conversion. Visualization Analysis Use MATLAB plotting functions like `imagesc` or `surf` to generate spectrograms. The x-axis represents time, the y-axis represents frequency, and color intensity reflects signal strength. Additional formatting can include `colorbar` for intensity reference and axis labeling for clarity. In practical implementation, achieving optimal STFT performance requires careful parameter tuning of window length, overlap ratio, and FFT points to balance resolution and computational efficiency. This method has significant applications in speech analysis, vibration signal detection, and other time-frequency domains. MATLAB's Signal Processing Toolbox provides built-in functions like `spectrogram` for streamlined implementation, but custom code allows for greater flexibility in parameter control and algorithm customization.