signal-processing

I am quite new to signal processing so forgive me if I rant on a bit. I have download and installed FFTW for windows. The documentation is ok but I still have queries. My overall aim is to capture raw audio data sampled at 44100 samps/sec from the sound card on the computer (this task is already implemented using libraries and my code), and then perform the DFT on blocks of this audio data. I am only interested in finding a range of frequency components in the audio and I will not be performing any inverse DFT. In this case, is a real to real transformation all that is necessary, hence the

For class, we are trying to prove a simple example of the Lomb-Scargle Periodogram using the embedded package in scipy. There is little documentation on how to use this function, and I have not been able to find any help online. When I run the code, I get a value of ~6.3 for the main peak of the periodogram, as opposed to the expected ~23.3. The data that we are pulling from is a simple .dat file with lists of numbers. Here is the code, any ideas on what's happening? import scipy as sp import math as m import numpy as np from scipy.signal import lombscargle import pylab as plt from numpy

My signal is a static 1D pattern detected by the linear photodiode array with N pixels and pitch p. What units will I get along the X-axis after FFT to spectrum? If you have a signal f(x) with unit U depending on variable x with unit V. Then the continuous Fourier transform of f has unit UV and depends on a variable with unit 1/V. Example 1: f(x) is a Voltage with x being time. then the Fourier transform has unit Vs (or V/Hz) versus variable 1/s (or Hz). Example 2: f(x) is a power with x being space. Then the FT has unit Wm and the x axis (which is then a wavenumber) unit 1/m (this is probably