Speech Signal Analysis Using Short-Time Fourier Transform

In this article, we perform speech signal analysis using Short-Time Fourier Transform (STFT). This process involves windowing and framing operations on the signal. STFT is a fundamental signal processing technique that converts time-domain signals into frequency-domain representations, enabling better understanding of spectral characteristics. The implementation typically involves: - Windowing: Applying window functions (such as Hamming or Hanning windows) to reduce spectral leakage effects by tapering signal edges - Framing: Dividing long-duration signals into short-time segments (typically 20-40ms frames) for localized spectral analysis Key algorithmic steps include: 1. Frame blocking: Segmenting the signal into overlapping frames using frame_size and hop_size parameters 2. Window application: Multiplying each frame by a window function to minimize discontinuities 3. FFT computation: Performing Fast Fourier Transform on each windowed frame 4. Spectrum analysis: Calculating magnitude and phase spectra for each time segment The MATLAB implementation would utilize functions like: - buffer() for frame segmentation - hamming() or hanning() for window generation - fft() for Fourier transform computation - spectrogram() for visualization of time-frequency distribution This approach allows for precise analysis of time-varying spectral properties in speech signals, crucial for applications like speech recognition and audio processing.