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
A few comments: The Nyquist frequency is half the sampling rate. You are working with regularly sampled data, so you want a digital filter, not an analog filter. This means you should not use analog=True in the call to butter, and you should use scipy.signal.freqz (not freqs) to generate the frequency response. One goal of … Read more
I’m adding another answer since it strikes me that Steven’s is not quite correct and Horchler’s suggestion to look inside function awgn is a good one. Either MATLAB or Octave (in the communications toolbox) have a function awgn that adds (white Gaussian) noise to attain a desired signal-to-noise power level; the following is the relevant … Read more
I found an answer to my question, just sharing for everyone who stumbles upon this. There is an algorithm called “Asymmetric Least Squares Smoothing” by P. Eilers and H. Boelens in 2005. The paper is free and you can find it on google. def baseline_als(y, lam, p, niter=10): L = len(y) D = sparse.csc_matrix(np.diff(np.eye(L), 2)) … Read more
Looking at the screenshots of the source audio files, one simple way to detect a change in sound level would be to do a numerical integration of the samples to find out the “energy” of the wave at a specific time. A rough algorithm would be: Divide the samples up into sections Calculate the energy … Read more
The following code explains how to generate a frequency-variable sin wave. freq1=20; %start freq freq2=200; %end freq dur=1; %duration of signal in seconds freq=@(t)freq1+(freq2-freq1)/dur*t; %another example, may help to understand the code %dur=2 %freq=@(t)heaviside(t-1)*10+heaviside(t-1.5)*-9; %Integerate over the time-local frequency, gives the average frequency until t which later on gives the sin with the right phase … Read more
You could use interp3 (since you want to interpolate 3D data): im=rand(2,3,4); %% input image ny=3;nx=3;nz=5; %% desired output dimensions [y x z]=… ndgrid(linspace(1,size(im,1),ny),… linspace(1,size(im,2),nx),… linspace(1,size(im,3),nz)); imOut=interp3(im,x,y,z);
EDIT: It seems that mov_average_expw() function from scikits.timeseries.lib.moving_funcs submodule from SciKits (add-on toolkits that complement SciPy) better suits the wording of your question. To calculate an exponential smoothing of your data with a smoothing factor alpha (it is (1 – alpha) in Wikipedia’s terms): >>> alpha = 0.5 >>> assert 0 < alpha <= 1.0 … Read more
JLayer should do everything you need. It’s not dead, it’s just stable. The author finished it up quite a long time ago and the MP3 format has not seen much change since. You’ll notice that his MP3SPI codebase is a little more recent. What MP3SPI does, is that translates JLayer’s abilities into JavaSound APIs. Thus … Read more
All the previous answers are correct if you want a technically accurate or scientifically valuable answer. But if you just want a general estimation of comparative loudness, like if you want to check whether the dog is barking or whether a baby is crying and you want to specify the threshold in dB, then it’s … Read more