1. A method for automatically determining class types of input signals having unknown class types, comprising: a) learning, by a neural network including multiple stacked sparse denoising autoencoders (SSDA) with weighted connections, features associated with a plurality of different observed signals having respective different known class types, wherein step a) comprises adjusting assigned weights of the connections based on the features of the plurality of different observed signals; b) refining, by a softmax component, the adjusted weights of the connections based on outputs of the SSDA; c) recognizing, by the SSDA, features of the input signals having unknown class types that at least partially match at least some of the features associated with the plurality of different observed signals having respective different known class types; d) determining, by the softmax component, probabilities that each of the input signals have each of the known class types based on strengths of matches between recognized features of each of the input signals and the features associated with the plurality of different observed signals; and e) classifying, by the softmax component, each of the input signals as having one of the respective different known class types based on a highest determined probability for each input signal in a manner that is accurate in noisy environments.