4/15/2024 0 Comments Sonic visualiser filter![]() The Auger & Flandrin paper instead comes from a world that summarises a spectrogram as a two-dimensional Wigner-Ville distribution filtered with a smoothing window leading to a time-frequency representation of the Cohen’s class. The language used in a publication like the DAFx book is typical in this world. There is nothing particularly mathematical about the implementation of this, and any intuition used by the programmer is a mixture of the visual and techniques from the world of engineering. The smoothing window is because your Fourier transform - a thing which matches up sinusoids of different frequencies against a signal to identify which ones would add up to it - operates on an infinite signal, consisting of the input you give it repeated forever in both directions: this will have a discontinuity each time it wraps around, and the smoothing window removes some of the frequency artifacts from these discontinuities. The short slices are because you want a fixed, smallish number of output bins, and you have various tradeoffs - time and frequency resolution and computational efficiency - to consider in that. For a programmer, a spectrogram comes from taking short overlapping slices of a sampled signal, multiplying each by a smoothing window shape, applying a short-time Fourier transform, and taking the magnitudes of the complex output bins to get one column of the spectrogram per slice of input. I have since realised this is partly because it isn’t all that clear with its notation, but there is also a big gap between the naive programmer’s view (that’s mine) of a spectrogram and the mathematical analysis used in the paper. I read this paper about 15 years ago and didn’t understand it. Illustration from Auger & Flandrin (1995) This crunchy publication (21 pages, dozens of equations and figures) took a pleasing idea - replacing the familiar grid-format time-frequency spectrogram with a field of precisely localised points calculated using both magnitude and phase of the frequency bins, rather than only magnitude as a traditional spectrogram does - and set out the mathematics of applying it to a number of different time-frequency and time-scale representations. Sonic Visualiser is an impressive free tool for musicians that will aid music learning and analysis of audio files.Patrick Flandrin is a physicist and signal-processing researcher whose name I first encountered as co-author (with François Auger) of a 1995 IEEE Transactions on Signal Processing paper called “Improving the Readability of Time-Frequency and Time-Scale Representations by the Reassignment Method”. ![]() Unfortunately though, Sonic Visualiser cannot support VST plugins directly because Steinberg's VST license is incompatible with Sonic Visualiser's GPL license although both Mac and Windows users can get support using the Audacity VST Enabler. It can load audio files in WAV, Ogg and MP3 formats, view their waveforms in spectrograms and allows you to annotate audio data by adding time points and markers. ![]() Although its free, Sonic Visualiser is very powerfu. ![]() ![]() Sonic Visualiser can use LADSPA and DSSI effects plugins and Windows users are at a slight advantage over Mac users as they can download some LADSPA plugins from the Audacity plugin page. ![]()
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