Jabarkan Time-frequency transforms dari Analisis Fourier dalam bahasa sunda

Time-frequency transforms such as the short-time Fourier transform, wavelet transforms, chirplet transforms, and the fractional Fourier transform try to obtain frequency information from a signal as a function of time (or whatever the independent variable is), although the ability to simultanéously resolve frequency and time is limited by an (mathematical) uncertainty principle.

Interpretation in terms of time and frequency

In terms of signal processing, the transform takes a time series  representation of a signal function and maps it into a frequency spectrum, where ω is angular frequency.  That is, it takes a function in the time domain into the frequency domain; it is a decomposition of a function into harmonics of different frequencies.

When the function f is a function of time and represents a physical signal, the transform has a standard interpretation as the frequency spectrum of the signal. The magnitude of the resulting complex-valued function F  at frequency ω represents the amplitude of a frequency component whose initial phase is given by:  arctan (imaginary part/real part).

However, it is important to réalize that Fourier transforms are not limited to functions of time, and temporal frequencies.  They can equally be applied to analyze spatial frequencies, and indeed for néarly any function domain.