MATLAB Source Code for Short-Time Analysis of Speech Signals

Short-time analysis of speech signals primarily involves the following aspects: framing, short-time energy, short-time average magnitude, short-time zero-crossing rate, short-time autocorrelation function, short-time magnitude difference, cepstrum, complex cepstrum, LPC coefficients, and LPC spectral estimation. These analysis procedures are guaranteed to be high-quality implementations and represent fundamental programs assigned by my supervisor after guaranteed graduate admission. When performing short-time analysis of speech signals, we first implement framing processing by dividing the signal into short-time frames using overlapping window functions (typically Hamming or Hanning windows). For each frame, we calculate short-time energy and short-time average magnitude to characterize amplitude variations, along with short-time zero-crossing rate to measure frequency content. The implementation typically involves windowing the signal and computing these features using vectorized operations for efficiency. Subsequently, we compute the short-time autocorrelation function for each frame to analyze periodicity and pitch information, and short-time magnitude difference to capture rapid amplitude changes. These features are crucial for analyzing frequency and amplitude variations in the signal. The autocorrelation function can be efficiently computed using FFT-based methods to reduce computational complexity. Additionally, we employ cepstrum and complex cepstrum analysis to extract spectral characteristics by applying inverse Fourier transform to the log magnitude spectrum. This helps in separating excitation and vocal tract components. Furthermore, we calculate Linear Predictive Coding (LPC) coefficients using methods like Levinson-Durbin recursion for efficient speech modeling, and perform LPC spectral estimation to approximate the speech spectrum through autoregressive modeling. These fundamental programs serve as essential tools for speech signal processing and analysis, providing the foundation for further research and applications in speech technology. Each module includes proper parameter initialization, error handling, and visualization capabilities for comprehensive analysis.