👉 BW Math, or Basic Wavelet Analysis, is a mathematical framework that uses wavelets to analyze and represent signals or functions in both time and frequency domains simultaneously. Unlike traditional Fourier analysis, which decomposes signals into sine and cosine waves (Fourier Transform), BW Math employs wavelets—localized functions that can capture transient features and varying scales of information. This approach allows for efficient compression, noise reduction, and feature extraction in applications ranging from image processing to audio analysis. By adapting the resolution (or scale) at different points in the signal, BW Math can effectively highlight details that are crucial for understanding complex data patterns, making it a powerful tool in signal processing and machine learning.
