1. A digital sound identification system for suppressing interference, the system comprising: non-volatile memory for storing a Markov model; stored program memory storing processor control code; a sound data input; a processor coupled to said sound data input, to said working memory, and to said stored program memory for executing said processor control code, and wherein said processor control code comprises code to: input, from said sound data input, first sample sound data for a first sound to be identified, wherein said sound to be identified is one of breaking glass, an alarm, gunshot, an aggressive vocalisation, and a sound of movement; said first sample sound data defining first sample frequency domain data, said first sample frequency domain data defining an energy of said first sample in a plurality of frequency ranges; perform a multiple component decomposition of said first sample frequency domain data by applying a succession of windows to said sound sample data to construct a time frequency matrix representing said first sample sound data; generate from said time frequency matrix a first set of mean and variance values for said breaking glass, alarm, gunshot, aggressive vocalisation, or sound of movement, wherein said first set of mean and variance values defines states of at least a first Markov model of said breaking glass, alarm, gunshot, aggressive vocalisation, or sound of movement; determine a state transition probability matrix for transitions between said states of said first Markov model, said state transition probability matrix characterising how a frequency distribution of said breaking glass, alarm, gunshot, aggressive vocalisation, or sound of movement changes over time; store said first Markov model including said state transition probability matrix in said non-volatile memory; input interference sound data defining interference frequency domain data; adjust said first Markov model using a multiple component decomposition of said interference frequency domain data, wherein said adjusting has the effect of suppressing correlated harmonics in interference in said first sample sound data; input third sound data defining third sound frequency domain data; perform a multiple component decomposition of said third sound frequency domain data; determine a probability of said multiple component decomposition fitting at least said first Markov model using said state transition probability matrix; and output sound identification data dependent on said probability.