1. A method for discriminating between at least two classes of events, the method comprising: receiving a set of frames including an input signal; determining at least two different feature vectors for each of the frames, wherein a first feature vector of the at least two different feature vectors is based on energy in different frequency bands, and a second feature vector of the at least two different feature vectors is based on an acoustic model; preclassifying the at least two different feature vectors using respective sets of preclassifiers trained for the at least two classes of events, wherein the preclassifying occurs separately from a training of the sets of preclassifiers; determining at least one distance between outputs of each of the sets of preclassifiers; comparing the at least one distance to at least one predefined threshold, wherein the comparing occurs after determining at least one distance between outputs of each of the sets of preclassifiers is performed; determining values for at least one weighting factor based on the at least one distance, using a formula dependent on the comparison; calculating a combined feature vector for each of the frames by applying the at least one weighting factor to the at least two different feature vectors; and classifying the combined feature vector using a set of classifiers trained for the at least two classes of events.