Tidfrekvensanalys

Tidfrekvensanalys is a method used in signal processing to study how the frequency content of a signal changes over time. Unlike traditional frequency analysis, which provides a single frequency spectrum for an entire signal, time-frequency analysis offers a more detailed view by showing which frequencies are present at specific moments in time. This is particularly useful for analyzing non-stationary signals, where the frequency composition evolves.

Several techniques fall under the umbrella of time-frequency analysis. One common approach is the Short-Time Fourier Transform (STFT). In STFT, the signal is divided into small, overlapping segments, and a Fourier Transform is applied to each segment. This results in a representation that shows how the frequency spectrum changes from one segment to the next. Another popular method is the Wigner-Ville distribution, which provides a high-resolution time-frequency representation but can suffer from cross-terms that may complicate interpretation. Wavelet analysis is another significant technique that uses basis functions called wavelets, which are localized in both time and frequency, to decompose the signal.

The output of time-frequency analysis is often visualized as a spectrogram or a scalogram, which is a 2D plot where one axis represents time, the other represents frequency, and the intensity or color at each point indicates the magnitude of that frequency component at that specific time. Time-frequency analysis finds applications in a wide range of fields, including speech recognition, audio processing, radar, seismic data analysis, and medical signal analysis (like EEG and ECG). Its ability to reveal transient phenomena and evolving spectral characteristics makes it an indispensable tool for understanding complex signals.