![]() ![]() 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) When expressing a power ratio, the number of decibels is ten times its logarithm to base 10. 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. Sonic Visualiser eats that.It seems I have an issue in the implementation of a function to create a frequency spectrum from an audio file. The work Sonic Visualiser does is intrinsically processor-hungry and (often) memory-hungry, but the aim is to allow you to work with long audio files on machines with modest CPU and memory where reasonable. ![]() Sonic Visualiser is pervasively multithreaded, loves multiprocessor and multicore systems, and can make good use of fast processors with plenty of memory. Even if you have to wait for your results to be calculated, you should be able to do something else with the audio data while you wait. In this respect, Sonic Visualiser aims to resemble a consumer audio application. The user interface should be simpler to learn and to explain than the internal data structures. To facilitate ready comparisons between different kinds of data, for example by making it easy to overlay one set of data on another, or display the same data in more than one way at the same time. To provide the best available core waveform and spectrogram audio visualisations for use with substantial files of music audio data. The design goals for Sonic Visualiser are: Time-stretch playback, slowing right down or speeding up to a tiny fraction or huge multiple of the original speed while retaining a synchronised display.Įxport audio regions and annotation layers to external files. Select areas of interest, optionally snapping to nearby feature locations, and audition individual and comparative selections in seamless loops. Play back the audio plus synthesised annotations, taking care to synchronise playback with display. Import note data from MIDI files, view it alongside other frequency scales, and play it with the original audio. Import annotation layers from various text file formats. Run feature-extraction plugins to calculate annotations automatically, using algorithms such as beat trackers, pitch detectors and so on. View the same data at multiple time resolutions simultaneously (for close-up and overview). Overlay annotations on top of one another with aligned scales, and overlay annotations on top of waveform or spectrogram views. Look at audio visualisations such as spectrogram views, with interactive adjustment of display parameters.Īnnotate audio data by adding labelled time points and defining segments, point values and curves. Load audio files in WAV, Ogg and MP3 formats, and view their waveforms. Sonic Visualiser contains features for the following:
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