Analyze Menu

Analyse contains tools for finding out about the characteristics of your audio, or labeling key features. Plugins that accept input but produce no output will also be placed in the Analyze menu.

Plot Spectrum

This takes the selected audio (which is a set of sound pressure values at points in time) and converts it to a set of freqencies and amplitudes that, if mixed back together again, would make the same sound. This is done using a complicated piece of maths known as a Fast Fourier Transform or FFT.

This gives a value for each narrow band of frequencies that represents how much of those frequencies is present. This values are then interpolated to create the graph which is drawn on screen.

The drop-down lists let you configure the way the plot is calculated.

Algorithm

What processing is done on the audio data to generate the graph. The default Spectrum plots the FFT of the data as described above. The Autocorrelation options measure to what extent the sound repeats itself. This is done by taking two copies of the audio, and moving one forward by one sample. The two copies are then multiplied together, and all the values added up. This is repeated for two samples difference and so on, up to the number of samples in the size option. This gives a small result if the waveform is random (e.g. noise) and a large result if it is repetative (like a musical note). By looking at the peaks in the plot you can determine the key frequencies present, even if there is a lot of noise.

Size

This controls how many frequency divisions are used for the spectrum, or how many samples are used for the autocorrelation. In the FFT a large number gives accurate frequencies (narrow bands) but needs a longer audio sample, and so averages the result over a longer period of time. In the autocorrelation, a large size looks for repeating patterns over a larger range of time offsets, and so will detect lower frequency patterns.

Function

This determines what mathematical function is used to pre-process the data. The basic forms of the FFT and autocorrelation algorithm require infinitely long sections of audio to work on, and so take infinitely long to complete. Hence the available audio must be pre-processed so that the fact the audio has finite length has minimum effect. Rectangular window is the simplest - it just cuts off the block of sample s with a sharp cut, and so leaves a sharp click at each end of the data. This means the results are often poor with a lot of random frequencies in them. Hamming, Hanning, and Bartlett windows do a smooth fade in and out of the audio data, and so give cleaner, more accurate results. Can someone explain what the differences are? I've only done Hamming!

Axis

You can choose between frequencies on a logarithmic scale or a linear scale for the Spectrum plot here.

Beatfinder

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