Actually, you don’t have to swap the different quadrants, it’s needed only if you’re a human and want a more natural looking visualization of the FFT result (i.e. with the 0 frequency in the middle, negative frequencies left/bottom and positive frequencies up/right). To invert the FFT, you need to pass the result of the forward … Read more
Correct functions: import numpy as np from scipy import fftpack as scipy_fftpack from scipy import fft as scipy # FFTPACK RFFT 2D def fftpack_rfft2d(matrix): fftRows = scipy_fftpack.fft(matrix, axis=1) fftCols = scipy_fftpack.fft(fftRows, axis=0) return fftCols # FFTPACK IRFFT 2D def fftpack_irfft2d(matrix): ifftRows = scipy_fftpack.ifft(matrix, axis=1) ifftCols = scipy_fftpack.ifft(ifftRows, axis=0) return ifftCols.real You calculated the 2D FFT … Read more
Before we actually go into what that MATLAB command does, you probably want to know what a spectrogram is. That way you’ll get more meaning into how each parameter works. A spectrogram is a visual representation of the Short-Time Fourier Transform. Think of this as taking chunks of an input signal and applying a local … Read more
Indeed, if the frame lasts T seconds, the frequencies of the DFT are k/T Hz, where k is an integer. As a consequence, oversampling does not improve the accuracy of the estimated frequency, as long as these frequencies are identifed as maxima of the magnitude of the DFT. On the contrary, considering longer frames lasting … Read more
In the end, the most simple thing (calculating the coefficient with a riemann sum) was the most portable/efficient/robust way to solve my problem: import numpy as np def cn(n): c = y*np.exp(-1j*2*n*np.pi*time/period) return c.sum()/c.size def f(x, Nh): f = np.array([2*cn(i)*np.exp(1j*2*i*np.pi*x/period) for i in range(1,Nh+1)]) return f.sum() y2 = np.array([f(t,50).real for t in time]) plot(time, y) … Read more
There are quite a few similar/related questions on SO already which are well worth reading as the answers contain a lot of useful information and advice, but in essence you need to do this: Convert the audio data to the format required by FFT (e.g. int -> float, with separate L/R channels); Apply suitable window … Read more
The aubio libraries have been wrapped with SWIG and can thus be used by Python. Among their many features include several methods for pitch detection/estimation including the YIN algorithm and some harmonic comb algorithms. However, if you want something simpler, I wrote some code for pitch estimation some time ago and you can take it … Read more
The Discrete Fourier transform (DFT) and, by extension, the FFT (which computes the DFT) have the origin in the first element (for an image, the top-left pixel) for both the input and the output. This is the reason we often use the fftshift function on the output, so as to shift the origin to a … Read more
Python provides several api to do this fairly quickly. I download the sheep-bleats wav file from this link. You can save it on the desktop and cd there within terminal. These lines in the python prompt should be enough: (omit >>>) import matplotlib.pyplot as plt from scipy.fftpack import fft from scipy.io import wavfile # get … Read more
FFTW is the ‘fastest fourier transform in the west’, and has some Java wrappers: http://www.fftw.org/download.html Hope that helps!