Speech Short-Time Spectrum Analysis
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Resource Overview
Detailed Documentation
This project implements the principles and methods of speech short-time spectrum analysis using MATLAB, involving several key steps: signal preprocessing, window function selection, Fourier transform computation, and spectrum visualization. Through spectral analysis and spectrogram analysis of speech signals, corresponding analytical results and conclusions are obtained. Implementation typically involves using MATLAB's signal processing functions such as hamming() for windowing, fft() for Fourier transform computation, and spectrogram() for time-frequency analysis. The algorithm processes audio frames by applying overlapping windows to capture temporal variations, then computes magnitude spectra using FFT to reveal frequency characteristics. Key parameters include window size (typically 20-30ms), overlap ratio (commonly 50-75%), and FFT points (often 1024 or 2048) for optimal time-frequency resolution balance.
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