Performing Short-Time Fourier Transform on Signals

In signal processing, the Short-Time Fourier Transform (STFT) is commonly employed to analyze signal frequency and phase characteristics. By applying STFT to signals, we obtain time-localized frequency and phase information, enabling better understanding of temporal variations and signal properties. In MATLAB implementation, the stft function allows configuring key parameters like window size (e.g., hamming(256)) and overlap percentage to balance time-frequency resolution. The transform works by segmenting signals into short-time frames using sliding windows, applying Fourier transform to each frame, and generating spectrograms through magnitude squared calculations. Adjustable factors including window type (hanning/blackman), length, and hop size facilitate adaptation to diverse signal types and processing requirements, thereby improving analysis accuracy and computational efficiency. The inverse STFT (istft) function enables perfect signal reconstruction when using symmetric windows with adequate overlap.