Explanation of binning in frequency analysis post-FFT I am creating an Android app which records sound for t seconds at a sampling rate of 44.1kHz with a buffer size of 8192 (16 bit mono samples). I need to plot a graph of amplitude against frequency, and so first I need to run an FFT over the buffer each time it is full (which happens multiple times every second, more so with an increasing sampling rate). However, after I run the FFT I understand I need to 'bin' the frequencies and then apply an A-weighting to each bin. From the Nyquist criterion, I understand that I only need to look at the first half of the frequencies, and disregard the first corresponding to the DC component. However, I am unsure of what 'binning' actually means and would like some clarification as to what it is, and how to choose what goes in what bin.

Please advise :) .
In its simplest form, the 'binning' process consists in summing the energies (squared magnitude) within groups of adjacent FFT values. This will give you the total energy in a set of disjoint frequency bands.

A more elaborate form consists in using overlapping triangle filter banks - you compute a weighted sum of the energy in a range of FFT bins to get a number which can be interpreted as the energy measured at the output of a band-pass filter of a given center frequency/width. For example, one can use this technique to build a visualization which uses a 1/3 octave scale on the frequency axis.