Wavelet Packet Analysis for Energy Feature and Power Spectrum Extraction

Wavelet packet analysis extracts energy features and power spectrum from signals. In signal processing, wavelet packet analysis serves as an effective methodology for extracting and analyzing energy characteristics and power spectrum of signals. This technique decomposes signals into different frequency bands through wavelet packet decomposition algorithms (typically implemented using functions like wpdec in MATLAB), then analyzes the energy distribution and power spectrum within each frequency band. The computational process involves calculating energy values for each decomposed node using squared L2-norm of coefficients, followed by power spectral density estimation through Fourier transform or periodogram methods. By revealing signal features and properties through multi-resolution analysis, this approach finds applications across various domains including speech signal processing (e.g., vocal feature extraction), image processing (texture analysis), and vibration signal analysis (mechanical fault detection). Therefore, understanding and mastering wavelet packet analysis, including implementation workflows like decomposition level selection, basis function optimization, and energy thresholding, is crucial for in-depth comprehension of signal characteristics and properties in modern digital signal processing systems.