👉 Noise math is a branch of signal processing that deals with the analysis and manipulation of signals contaminated by noise. It involves understanding how noise affects signal quality and developing methods to mitigate its impact. Key concepts include the power spectral density (PSD), which describes how noise power is distributed across different frequencies, and the Wiener filter, a fundamental tool that estimates the original signal by minimizing the mean square error between the estimated and true signals. Noise math also explores techniques like spectral subtraction, adaptive filtering, and machine learning approaches to enhance signal clarity in noisy environments. This field is crucial for applications ranging from telecommunications and audio processing to medical imaging, where maintaining signal integrity in the presence of noise is essential.
