scipy provides a correlation function which will work fine for small input and also if you want non-circular correlation meaning that the signal will not wrap around. note that in mode=”full” , the size of the array returned by signal.correlation is sum of the signal sizes minus one (i.e. len(a) + len(b) – 1), so … Read more
Here is how you implement a low-pass filter using convolution: double[] signal = (some 1d signal); double[] filter = [0.25 0.25 0.25 0.25]; // box-car filter double[] result = new double[signal.Length + filter.Length + 1]; // Set result to zero: for (int i=0; i < result.Length; i++) result[i] = 0; // Do convolution: for (int … Read more
You ask two questions: How to downsample from 22kHz to 8kHz? How to convert from float [-1,1] to 16-bit int and back? Note that the question has been updated to indicate that #1 is taken care of elsewhere, but I’ll leave that part of my answer in in case it helps someone else. 1. How … Read more
This is a pretty imperfect solution, especially since some of the noise is embedded in the same frequency range as the voice you hear on the file, but here goes nothing. What I was talking about with regards to the frequency spectrum is that if you hear the sound, the background noise has a very … 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
Solved, solution: Filter data first: window = signal.general_gaussian(51, p=0.5, sig=20) filtered = signal.fftconvolve(window, data) filtered = (np.average(data) / np.average(filtered)) * filtered filtered = np.roll(filtered, -25) Then use angrelextrema as per rapelpy’s answer. Result:
Here is a graphic that illustrates the threshold approach to note onset detection: This image shows a typical WAV file with three discrete notes played in succession. The red line represents a chosen signal threshold, and the blue lines represent note start positions returned by a simple algorithm that marks a start when the signal … Read more
My first suggestion is to not do your DSP implementation in Java. My second suggestion would be to roll your own simple DSP implementations yourself in Java. Why not to use Java: I have lots of experience writing DSP code over the last 10+ years… and almost none of the DSP code is in Java… … 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