Analysis techniques are those used to examine, understand and interpret the content of recorded sound signals. Sometimes these lead to visualisation methods, whilst at other times they may be used in specifying some form of further processing or measurement of the audio.

There is a general set of analysis techniques which are common to all audio signals, and indeed to many forms of data, particularly the traditional methods used for signal processing. We have already met and used the basic technique of decomposing sound into multiple sinusoidal components with the Fast Fourier Transform (FFT), and have considered forming a polynomial equation to replicate audio waveform characteristics through linear prediction (LPC), but there are many other useful techniques we have not yet considered.

Most analysis techniques operate on analysis windows, or frames, of input audio. Most also require that the analysis window is a representative stationary selection of the signal (stationary in that the signal statistics and frequency distribution do not change appreciably during the time duration of the window – otherwise results may be inaccurate). We had discussed the stationarity issue in Section 2.5.1, and should note that the choice of analysis window size, as well as the choice of analysis methods used, depends strongly upon the identity of the signal being analysed. Speech, noise and music all have different characteristics, and while many of the same methods can be used in their analysis, knowledge of their characteristics leads to different analysis periods, and different parameter ranges of the analysis result.