fft

问题 I would like to calculate the correlation coefficient of 2 sound samples using AForge 2.2.5 . I've read from here the formula to calculate Cross Correlation. And here I've read about the formula to calculate the correlation coefficient. This is currently what I have: Prior to calling CrossCorrelation(), FFT has been performed. static Complex[] CrossCorrelation(Complex[] ffta, Complex[] fftb) { var conj = ffta.Select(i => new Complex(i.Re, -i.Im)).ToArray(); for (int a = 0; a < conj.Length; a+

问题 I would like to calculate the correlation coefficient of 2 sound samples using AForge 2.2.5 . I've read from here the formula to calculate Cross Correlation. And here I've read about the formula to calculate the correlation coefficient. This is currently what I have: Prior to calling CrossCorrelation(), FFT has been performed. static Complex[] CrossCorrelation(Complex[] ffta, Complex[] fftb) { var conj = ffta.Select(i => new Complex(i.Re, -i.Im)).ToArray(); for (int a = 0; a < conj.Length; a+

问题 After researching and a lot of trials-and-errors, I have come to a point that I can construct a spectrogram which I think it has element of rights and wrongs . 1. First, I read .wav file into a byte array and extract only the data part. 2. I convert the byte array into a double array which takes the average of right and left channels. I also notice that 1 sample of 1 channel consists of 2 bytes. So, 4 bytes into 1 double. 3. For a certain window size of power of 2, I apply FFT from here and

问题 I am trying to use FFT to decode morse code, but I'm finding that when I examine the resulting frequency bin/bucket I'm interested in, the absolute value is varying quite significantly even when a constant tone is presented. This makes it impossible for me to use the rise and fall around a threshold and therefore decode audio morse. I've even tried the simple example that seems to be copied everywhere, but it also varies... I can't work out what I'm doing wrong, and my maths is not clever

问题 I've just started to learn about images frecuency domain. I have this function: def fourier_transform(img): f = np.fft.fft2(img) fshift = np.fft.fftshift(f) magnitude_spectrum = 20*np.log(np.abs(fshift)) return magnitude_spectrum And I want to implement this function: def inverse_fourier_transform(magnitude_spectrum): return img But I don't know how. My idea is to use magnitude_spectrum to get the original img . How can I do it? 回答1: You are loosing phases here: np.abs(fshift) . np.abs takes

问题 Some days ago I came across this answer about the usage of the FFT In the answer there's a piece of code like this: w = np.fft.fft(data) freqs = np.fft.fftfreq(len(w)) I read about the function fftfreq in the numpy documentation (here) and i found that it returns an array with the following content: f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is odd In my case, the d var is equal to 1 and n is an even number. So my

问题 I am new to the fourier theory and I've seen very good tutorials on how to apply fft to a signal and plot it in order to see the frequencies it contains. Somehow, all of them create a mix of sines as their data and i am having trouble adapting it to my real problem. I have 242 hourly observations with a daily periodicity, meaning that my period is 24. So I expect to have a peak around 24 on my fft plot. A sample of my data.csv is here: https://pastebin.com/1srKFpJQ Data plotted: My code: data

问题 I want to record sound of a machine and want to detect if its present in another sound file that I record. I am able to record the sound of the machine and the sound file that I want to detect its presence. I am also able to take fft of the sound files, however I dont know If I should check specific frequencies or check it in time intervals. Can I implement it with aubio or is there another way? silent room silent room + sound 来源： https://stackoverflow.com/questions/53305661/detecting

问题 The Fourier transform of the convolution (with stride 1) of two images is equivalent to point-wise multiplication of their individual Fourier transforms. I need to perform stride-'n' convolution using the above FFT-based convolution. For some reasons I need to operate in the frequency domain itself after taking the point-wise product of the transforms, and not come back to space domain by taking inverse Fourier transform, so I cannot drop the excess values from the inverse Fourier transform

问题 The Fourier transform of the convolution (with stride 1) of two images is equivalent to point-wise multiplication of their individual Fourier transforms. I need to perform stride-'n' convolution using the above FFT-based convolution. For some reasons I need to operate in the frequency domain itself after taking the point-wise product of the transforms, and not come back to space domain by taking inverse Fourier transform, so I cannot drop the excess values from the inverse Fourier transform