Currently supports Python, JavaScript, and Rust, with many more to come (accepting requests).Allows commercial use without open source requirement.Access finer-customisations including HTML injection.Access Divided mode, enabling two sides to your diagram.Produce beautiful interactive Chord diagrams.those featured on the front page of Reddit. Get Chord Pro ¶Ĭlick here to get lifetime access to the full-featured chord visualization API, producing beautiful interactive visualizations, e.g. The size of the segments illustrates the numerical proportions, whilst the size of the arc illustrates the significance of the relationships 1.Ĭhord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching. The source code of sonic visualizer is on sourceforge, so it should be easy to check (sourceforge is not allowing me to set my tracking policy, so I am not going there myself).In a chord diagram (or radial network), entities are arranged radially as segments with their relationships visualised by arcs that connect them. This may or may not explain your scale difference. If you change the scalar to 20, you get this image: The decibel scales differ by a factor of two so that the related power and field levels change by the same number of decibels with linear loads. When expressing field (root-power) quantities, a change in amplitude by a factor of 10 corresponds to a 20 dB change in level. That is, a change in power by a factor of 10 corresponds to a 10 dB change in level. When expressing a power ratio, the number of decibels is ten times its logarithm to base 10. Two different scales are used when expressing a ratio in decibels, depending on the nature of the quantities: power and field (root-power). I suspect there may still be some smoothing of some kind (there are references to hops in the code, but I can't quite suss out what they're doing).Īs you noted, your two results differ by a constant factor that is approximately 2.įrom Wikipedia's entry on Decibel (my emphasis): It's still not an exact match, but it's much closer. With the two changes I have mentioned, the graphs compare as follows: I have not found a way to export their spectrum, but I have manually read off the first few values (note, not the dB value) as theirvalues = [ This leads to a change of calculation to s_mag = np.abs(values) * 2 / data_length They appear simply to divide by the size of the window in the code when calculating the magnitude. I have also verified this in the v4.0.1 source code (in svcore/base/AudioLevel.cpp, line 54) double dB = 10 * log10(multiplier) The first big difference is that they are using the "power ratio" definition of the decibel, from this Wikipedia page: Where is the problem with my script, why do I get a different result? The script converts the 32bit float audio file into a dBFS spectrum diagram, using the first 4096 samples as the window, as Sonic Visualizer does. Target_name = audio_file.parent / (audio_file.stem + '.png') Plt.semilogx(x_labels, flat_data, color='tab:blue', label='Spectrum (with filter)') Plt.semilogx(x_labels, s_dbfs, alpha=0.4, color='tab:blue', label='Spectrum') X_labels, s_dbfs = db_fft(data, frequency)įlat_data = savgol_filter(s_dbfs, 601, 3) S_mag = np.abs(values) * 2 / np.sum(weighting)įrequency, data = wavfile.read(str(audio_file)) If I match the curve from Sonic Visualizer to my script's output, it is obvious the conversion of the levels lacks some factor:Ī minimal version of my script, using the 'demo.wav' file above, looks like this: from pathlib import Pathįrequencies = np.fft.rfftfreq(data_length, d=1. So I assume, there is a problem in my script converting the FFT result to dBFS. At 100Hz, Sonic Visualizer is at -40dB and my Script is at -65dB. Now I tried to reproduce this result using my Python Script, but get a different result:Įverything looks right, except the scale of the dB values. Before I started, I analyzed the file using Sonic Visualizer, which got me the following result: I am working on a script which is creating a spectrum analysis from an audio file using SciPy and NumPy. You can download the 32bit float WAV audio file here. I ask this question in the hope someone will find the problem. It seems I have an issue in the implementation of a function to create a frequency spectrum from an audio file.
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