Short-Time Energy Method and Power Spectrum Analysis

In this documentation, we explore the analysis of one-dimensional signal sequences using two fundamental approaches: the short-time energy method and power spectrum analysis. The short-time energy method involves computing signal energy within sliding time windows, typically implemented using frame-based processing with overlap-add techniques. This approach helps track intensity variations by calculating the squared magnitude of signal segments, often using functions like buffer() for segmentation and vectorized operations for energy computation. Power spectrum analysis employs Fourier transform techniques (FFT implementation) to decompose signals into frequency components, revealing spectral characteristics through periodogram or Welch's method. Key functions like pwelch() or periodogram() can be utilized with proper windowing and averaging parameters. Following these analyses, waveform visualization techniques using plotting functions (plot(), stem(), or spectrogram()) enable intuitive result interpretation. These combined methodologies provide comprehensive insights into temporal and frequency-domain characteristics of 1D signals, facilitating better understanding of signal behaviors through integrated computational and visual approaches.