Here is a simple and effective linear filtering strategy to remove the horizontal line artifact: Outline: Estimate the frequency of the distortion by looking for a peak in the image’s power spectrum in the vertical dimension. The function scipy.signal.welch is useful for this. Design two filters: a highpass filter with cutoff just below the distortion … Read more
Pitch is not the same as peak magnitude frequency bin (which is what the FFT in the Accelerate framework might give you directly). So any peak frequency detector will not be reliable for pitch estimation. A low-pass filter will not help when the note has a missing or very weak fundamental (common in some voice, … Read more
There are two issues: the way you use the FFT, and the particular filter. Filtering is traditionally implemented as convolution in the time domain. You’re right that multiplying the spectra of the input and filter signals is equivalent. However, when you use the Discrete Fourier Transform (DFT) (implemented with a Fast Fourier Transform algorithm for … Read more
I found scipy.signal.fftconvolve, as also pointed out by magnus, but didn’t realize at the time that it’s n-dimensional. Since it’s built-in and produces the right values, it seems like the ideal solution. From Example of 2D Convolution: In [1]: a = asarray([[ 1, 2, 3], …: [ 4, 5, 6], …: [ 7, 8, 9]]) … Read more
Just like you stated, take the fft and multiply pointwise by its complex conjugate, then use the inverse fft (or in the case of cross-correlation of two signals: Corr(x,y) <=> FFT(x)FFT(y)*) x = rand(100,1); len = length(x); %# autocorrelation nfft = 2^nextpow2(2*len-1); r = ifft( fft(x,nfft) .* conj(fft(x,nfft)) ); %# rearrange and keep values corresponding … Read more
Python’s wave library will let you import the audio. After that, you can use numpy to take an FFT of the audio. Then, matplotlib makes very nice charts and graphs – absolutely comparable to MATLAB. It’s old as dirt, but this article would probably get you started on almost exactly the problem you’re describing (article … 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
Taking a step back… To get this working you MUST figure out a way to plot intermediate steps of this process. What you’re trying to do is not particularly hard, but it is error prone and fiddly. Clipping, windowing, bad wiring, aliasing, DC offsets, reading the wrong channels, the weird FFT frequency axis, impedance mismatches, … Read more
First you need to ensure that the result you are getting is correctly converted to a float/double. I’m not sure how the short[] version works, but the byte[] version only returns the raw byte version. This byte array then needs to be properly converted to a floating point number. The code for the conversion should … Read more