n represents the unwanted signal such as broad band white noise babble noise or subway noise and u2212 20 log u03b3 db is the target snr improvement in decibel a perceptually inspired cost function 35 then computes a distance between the target sound segment s u03b3n and the actually processed sound segment or signal as an example the sum of differences of a log spectrum on a bark frequency scale constitutes a preferred and relevant cost distance function other cost functions are also possible the goal of the training phase is to adapt the parameters of the environmental classifier such that the selected cost function 35 accumulated over all sound segments within the collection in database 30 is minimized the above mentioned adaptation scheme is a well known u201c machine learning u201d type of application we choose an environmental classifier that controls the parameters of the noise suppression agent or agents 5 10 15 and 20 such that the target y t s t g n t is obtained as closely as possible for the inputs x t s t n t the classifier 25 is therefore a parameterized learning machine such as a hidden markov model neural network fuzzy logic machine or any other machine with adaptive parameters and can be trained by learning mechanisms that are well known in the art such as back propagation see for example.