fft

问题 For convolution theorem F(x.y) = F(x)*F(y) However after implement it on python x = np.array([0,0,0,0,1, 2, 3, 4, 0 ,0,0,0]) y = np.array([0,0,0,0,-3, 5, -4, 0, 0, 0,0,0]) xy = x*y inverse_fft_xy = np.fft.ifft(np.convolve(np.fft.fft(x),np.fft.fft(y))) Will yield xy array([ 0, 0, 0, 0, -3, 10, -12, 0, 0, 0, 0, 0]) inverse_fft_xy array([ 0.00000000e+00, -8.70383905e-01, 1.65925305e-02, -8.90888514e-01, 7.07822398e-02, -8.80447879e-01, 1.19687210e-01, 3.09247006e+00, -9.54481834e+00, -5

问题 I am really having a hard time to understand how the fft-function in MATLAB works. According to: http://www.mathworks.de/help/matlab/math/fast-fourier-transform-fft.html fs/n is the distance between the sampled points in the Spectrum, where fs is the sampling frequency and n is the length of the signal. The code that they present, does extract the frequencies (although I do not know why), but according to the implementation of fft, that they present: http://www.mathworks.de/help/matlab/ref

问题 some days ago I posted my FFT-Test tool with some problems, so this here is my "remake" poste with almost the same problems, but different code. Problem: I´m trying to make a tool to read some sensor values and do the fft of these. I implemented a normal DFT, a FFT(Bluestein) and a FFT(Cooley-Tukey). It all works fine and the inverse transformation shows the input like it should. The only thing is when i plot the Frequenz/Amplitude graph i have a huge peak at the begining (not only at 0 like

问题 First, refer to How to plot temporal frequency as a function of spatial frequency from a MATLAB FFT2 output of a time-space image? for a bit more of a background to this question. Assuming in the case of this sample signal:- n = [0:1024]; signal = sin(2*pi*n/10) + sin(2*pi*n/20) + sin(2*pi*n/30); N = 2048; %At least twice of the n value X = abs(fft(signal,N)); X = fftshift(X); %normalise data F = [-N/2:N/2-1]/N; %normalise data - shift it to the correct frequency plot(F,X); The variable F

问题 this question is related to : DSP : audio processing : squart or log to leverage fft? in which I was lost about the right algorithm to choose. Now, Goal : I want to get all the frequencies of my signal, that I get from an audio file. Context: I use numpy, and scikits.audiolab. I made a lot of reading on the dsp subject, went to dspguru.com as well, read papers and nice blogs over the net. The code I use is this one : import numpy as np from scikits.audiolab import Sndfile f = Sndfile('first

问题 I'd like to generate a 2D image of arbitrary size containing randomly generated pink noise. Wikipedia suggests that the 2D generalization of pink noise will have energy that falls off as 1/f^2. I found some code on the MATLAB File Exchange that computes a 1D pink noise vector. But I don't know how to properly generalize it to two dimensions -- I'm not very familiar with the fft, and my naive attempt below produces complex vectors when I compute the ifft. function pink = pinkNoiseImage(nrow

问题 I got this python code for FFT calculation of a sound signal: from math import * from cmath import exp, pi def fft(x): N = len(x) if N <= 1: return x even = fft(x[0::2]) odd = fft(x[1::2]) return ([even[k] + exp(-2j * pi * k / N) * odd[k] for k in xrange(N / 2)] + [even[k] - exp(-2j * pi * k / N) * odd[k] for k in xrange(N / 2)]) N = 64 res = [sin(k) for k in xrange(N)] # Window function a = 2*pi/(N-1) for k in xrange(N): z = a * res[k] res[k] = 0.42659 - 0.49656*cos(z) + 0.076849*cos(2*z)

问题 Given a set of n particles electric charge carriers founds on the points (1,0), (2,0), .... (n,0) on a plane. The particle charge that found in point (i,0) is noted as Qi. the force that act on the particle is given by the formula: C is a Coulomb's constant. Give an algorithm to calculate Fi, for all of the particles in total complexity O(nlgn). Hint: use FFT algorithm. It seems that Fi is already divided to the even and odd points.. I thought about to divide each sum to calculate the FFT

问题 So I have this piano recording (in .wav format). I am able to do an FFT on the whole recording and identify frequencies. However, according to some articles I read, its best if the wav file is broken down into windows, where each window would include one particular note. For this I need to initially plot a "power envelope" of my time domain signal (considering the note average energy concept) therefore there'll be one increase and one decrease for each note and note onsets can be determined

问题 I need to equalize a music sample by modifying the FFT result. I know how to obtain the frequencies of each of the output imaginary numbers, the problem is modifying this values to obtain an "equalizer effect". I need to know how to scale this values. The size of the entry is 4096 samples, and the sample rate is 44100 HZ Thanks in Advance Alex 回答1: You just need to scale the frequency domain bins. Note that these are complex values which you need to multiply by a scalar value, so you need to