fft

问题 I am currently trying to compare a simple inverse filter to the wiener filter for deconvolution using matlab. My starting signal is exp(-t^2) and this is to be convolved with a rect that is nonzero for times -.5 to .5. I am introducing noise with amplitude in the range -.5 to .5. Defining my time domain to frequency domain mapping: f = exp(-t^2) => F s = rect => R c = f*s => C r = noise (see above) => R with noise c becomes: c = f*s + n => C = FxS + N For the first approach I am simply taking

问题 I am manually initializing a state in the 2d frequency domain by setting the real components of certain modes in a 16x16 data set. I then perform a 2d IDFT to acquire the real domain data. This all works as expected. I then perform a DFT on the real domain data to get back (what should be) identical frequency modes to those that I manually initialized. However, they come back with their amplitudes halfed, and their vertical frequencies "mirrored". To illustrate: Input modes: k[1, 0]: 32 + 0i

问题 Log polar transform is commonly used in image with phase correlation using Fourier transform for estimating rotation and translation etc. However, I am little confusing how to apply it in audio signal. I am trying to estimate time shift between two audio signal through log polar transform (LPT) and phase correlation. I applied LPT using https://en.wikipedia.org/wiki/Log-polar_coordinates in audio signal and plotter using matlab with polar(theta,rho), until here, there is no problem, problem

问题 I am trying to store the results of FFT calculations in a Pandas data frame: ft = pd.DataFrame(index=range(90)) ft['y'] = ft.index.map(lambda x: np.sin(2*x)) ft['spectrum'] = np.fft.fft(ft['y']) ft['freq'] = np.fft.fftfreq(len(ft.index)).real ft['T'] = ft['freq'].apply(lambda f: 1/f if f != 0 else 0) Everything seems to be working fine until the last line: the column T which is supposed to store periods has for some reason all the columns of the frame, ie.: In [499]: ft.T[0] Out[499]: y 0j

问题 I am trying to store the results of FFT calculations in a Pandas data frame: ft = pd.DataFrame(index=range(90)) ft['y'] = ft.index.map(lambda x: np.sin(2*x)) ft['spectrum'] = np.fft.fft(ft['y']) ft['freq'] = np.fft.fftfreq(len(ft.index)).real ft['T'] = ft['freq'].apply(lambda f: 1/f if f != 0 else 0) Everything seems to be working fine until the last line: the column T which is supposed to store periods has for some reason all the columns of the frame, ie.: In [499]: ft.T[0] Out[499]: y 0j

问题 Please help me understand the following MATLAB code for Ideal Low pass filter. I am unable to understand the Part2 in the below code. Please explain me why we are doing like this. I have read the Rafael C. Gonzalez's Digital Image Processing Using Matlab 2E which explains my question but I couldn't understand properly. It will be helpful if someone could explain me clearly. Note: Dogbert, my understanding is that applying transform to an image help to separate low and high frequency

问题 i have a cufftcomplex data block which is the result from cuda fft(R2C). i know the data is save as a structure with a real number followed by image number. now i want to get the amplitude=sqrt(R*R+I*I), and phase=arctan(I/R) of each complex element by a fast way(not for loop). Is there any good way to do that? or any library could do that? 回答1: Since cufftExecR2C operates on data that is on the GPU, the results are already on the GPU, (before you copy them back to the host, if you are doing

问题 i have a cufftcomplex data block which is the result from cuda fft(R2C). i know the data is save as a structure with a real number followed by image number. now i want to get the amplitude=sqrt(R*R+I*I), and phase=arctan(I/R) of each complex element by a fast way(not for loop). Is there any good way to do that? or any library could do that? 回答1: Since cufftExecR2C operates on data that is on the GPU, the results are already on the GPU, (before you copy them back to the host, if you are doing

问题 I am trying to get a third-octave frequency spectrum of a time signal. The time signal is the acoustic pressure of rotational rotor noise which is harmonic. Its fundamental frequency is ff = n * N_b and for that reason, all frequencies should be multiples of ff. Using fft I get the expected result: Multiples of the fundamental frequency are the relevant frequencies in the spectrum. To get the third-octave frequency spectrum I wanted to use python acoustics, but the result of the function

问题 I am trying to take the fast Fast Fourier Transform. I am basing my calculation off of the Surge. I am having trouble getting correct results. When I take the fft of a 1000 hz sound I get something that looks like this. . When i take the same tone and use python I get something that looks way more correct. The python code looks like: import numpy as np import scipy.io.wavfile import numpy.fft import matplotlib.pyplot as plt FILENAME = 'beep.wav' fs, data = scipy.io.wavfile.read(FILENAME) data