ifft

I have just started writing a program to manipulate some audio in python. Before I write any filtering functions, I wanted to do a test to compare the input signal to the output signal after having the input signal go through an rfft and irfft. For some reason the output file has an incredible amount of gain in it (50db!) compared to the input file, and I can't figure out why this is happening. Here is the code: from scipy.io.wavfile import read, write from scipy.fftpack import rfft, irfft import numpy as np rate, input = read('5and10ksm.wav') transformed = rfft(input) output = irfft

I try to filter a signal using opencv's dft function. The way I try to this is taking the signal in time domain: x = [0.0201920000000000 -0.0514940000000000 0.0222140000000000 0.0142460000000000 -0.00313500000000000 0.00270600000000000 0.0111770000000000 0.0233470000000000 -0.00162700000000000 -0.0306280000000000 0.0239410000000000 -0.0225840000000000 0.0281410000000000 0.0265510000000000 -0.0272180000000000 0.0223850000000000 -0.0366850000000000 0.000515000000000000 0.0213440000000000 -0.0107180000000000 -0.0222150000000000 -0.0888300000000000 -0.178814000000000 -0.0279280000000000 -0

I want to remove the Gibbs artifact in a 1D signal by applying the Hamming filter on that in MATLAB. What I have is the k1 which is the signal in frequency domain. I can get the signal in time domain by applying DFT on k1 : s1 = ifft(ifftshift(k1)); This signal has Gibbs artifact. Now, I want to remove it by (A) multiplying Hamming filter to k1 in teh frequency domain and (B) convolving IFFT of Hamming filter with s1 in the spatial domain. I am expecting same output from both of these: % (A) Multiplying Hamming filter to `k1` n = size(k1,2); wk = hamming(n,'symmetric')'; k2 = wk.*k1; s2 = ifft

I perform the iFFT on a symmetric spectrum (using Python). Why is the result not an real-valued signal but contains complex values? # My symmetric spectrum spectrum = numpy.array( [1+1j,2+2j,3+3j,3-3j,2-2j] ) # Perform the iFFT print numpy.fft.ifft(spectrum) Output: (2.2+0.2j) (-1.98979431354+0.2j) (0.59464641547+0.2j) (-0.74743281997+0.2j) (0.942580718037+0.2j) Try it like this: # My symmetric spectrum spectrum = numpy.array( [0+0j,1+1j,2+2j,3+3j,0+0j,3-3j,2-2j,1-1j] ) # Perform the iFFT print numpy.fft.ifft(spectrum) Normally bin 0 is DC, bin N/2 is Nyquist, and both of these values are real

I am new in fast fourier transform (FFT) and does not have much idea, how it calculate in programming language such as C++. Here is method of FFT2D void FFT2D(Complex<double> *f, Complex<double> *F, int width, int height); It takes an input image f of size width * height and output the transformed coefficients into F. Hints: Image pixels are stored as three separate image color (R, G, B) planes, with each of them being represented by a 1D array of complex numbers. Suppose an image is of size width W and height H, then the color component values (R, G and B) of the pixels at image location (m,