{"id":1258,"date":"2018-10-12T06:56:53","date_gmt":"2018-10-12T06:56:53","guid":{"rendered":"https:\/\/labdeck.com\/?page_id=1258"},"modified":"2024-01-03T15:06:17","modified_gmt":"2024-01-03T15:06:17","slug":"spectral-analysis","status":"publish","type":"page","link":"https:\/\/labdeck.com\/spectral-analysis\/","title":{"rendered":"Spectral Analysis"},"content":{"rendered":"\n

MatDeck has several functions for spectral analysis as well as determining the frequency content of a signal. The following functions are fft-based non-parametric tools: periodogram(), powspectwelch() and spectrogram().<\/p>\n\n\n\n

In signal processing, a periodogram is used to estimate the spectral density power of the signal. The periodogram is a standard component in the more complex methods for spectral estimation, such as Welch\u2019s method for spectral estimation. Periodograms are calculated as a Fourier transform of an auto-correlation function that is FT{x(t)*x(-t)}= X(f)*X*(f)=abs(X(f))2. MatDeck contains a function called periodogram().<\/p>\n\n\n\n

Welch\u2019s method for estimating the power spectra is carried out by dividing the time signal into successive blocks, forming the periodogram for each block, and then averaging them. MatDeck contains a function, powspectwelch(), specifically designed for this purpose.<\/p>\n\n\n\n

A spectrogram is a visual representation of the spectrum of a signal, showing the frequency components as they vary with time. MatDeck contains a function, spectrogram(), which shows the results in a 3D graph.<\/p>\n\n\n\n

Spectral analysis examples:<\/h3>\n\n\n\n