1. A method for classification of an analog electrical signal using statistical models of training data, the method comprising: converting the analog electrical signal into a sequence of feature vectors, the feature vectors being multi-dimensional; quantizing a feature vector on a relevant subspace of a first statistical model as a function of a second statistical model, the first statistical model generated from a first training data set and the second statistical model generated from a second training data set; determining a likelihood ratio by dividing the likelihood that the quantized feature vector belongs to the second statistical model by the likelihood that the quantized feature vector belongs to the first statistical model; determining a log-likelihood ratio by computing a logarithm of the likelihood ratio; averaging a series of log-likelihood ratios; and classifying the analog electrical signal as belonging to the second statistical model if the average of the logarithms of likelihood ratios exceeds a predefined classification threshold.