tidsfrekvensmåling

Tidsfrekvensmåling, also known as time-frequency analysis, is a signal processing technique used to analyze signals that vary in frequency over time. Unlike traditional Fourier analysis, which provides a global frequency spectrum of a signal, time-frequency analysis reveals how the frequency content of a signal changes as time progresses. This is particularly useful for non-stationary signals, where the frequency components are not constant.

Several methods exist for time-frequency analysis. The Short-Time Fourier Transform (STFT) is a common approach where the signal is divided into small segments, and a Fourier Transform is applied to each segment. This provides a localized frequency representation. The Wigner-Ville distribution is another powerful method that offers high time-frequency resolution but can suffer from cross-term artifacts. Wavelet transforms provide a multi-resolution analysis, allowing for the examination of both high-frequency, short-duration events and low-frequency, long-duration components with varying degrees of precision.

Applications of tidsfrekvensmåling are widespread. In audio processing, it is used to analyze speech, music, and other sounds, identifying transient events and spectral changes. In biomedical engineering, it aids in the analysis of electroencephalograms (EEG) and electrocardiograms (ECG) to detect abnormalities. Radar and sonar systems utilize time-frequency analysis for target detection and characterization. It is also employed in seismic data processing, mechanical fault diagnosis, and many other fields where the dynamic behavior of signal frequencies is of importance. The choice of method depends on the specific characteristics of the signal and the desired resolution in both time and frequency domains.