Støysubtraksjon

Støysubtraksjon, also known as noise subtraction or noise reduction, is a signal processing technique used to remove unwanted noise from a signal. Noise can originate from various sources, including thermal noise in electronic components, environmental sounds, or interference from other signals. The goal of støysubtraksjon is to isolate the desired signal from the superimposed noise.

There are several methods for performing støysubtraksjon. One common approach is spectral subtraction, which estimates the noise spectrum during periods when only noise is present and then subtracts this estimate from the spectrum of the noisy signal. Another technique is Wiener filtering, which uses statistical properties of the signal and noise to create an optimal filter that minimizes the mean squared error between the estimated and original signal. More advanced methods include deep learning-based approaches that can learn complex noise patterns and effectively remove them.

The effectiveness of støysubtraksjon depends on several factors, including the nature of the noise, the signal-to-noise ratio, and the chosen algorithm. While it can significantly improve the quality of a signal, aggressive noise reduction can sometimes lead to artifacts or distortion of the desired signal. Applications of støysubtraksjon are widespread, ranging from audio and image processing to telecommunications and medical imaging, where enhancing clarity and intelligibility is crucial.