Signal processor employing neural network trained using evolutionary feature selection

A method for using a neural network to process speech signals, comprising:

• providing a corpus of data containing speech and non-speech;
• providing a collection of feature sets, each representing a different way to express a signal to be processed in terms of a feature vector;
• performing evolutionary feature selection using a genetic algorithm to evolve from the collection of feature sets, a subset collection of feature sets that maximizes a distribution distance between the speech and the non-speech;
• extracting from the corpus of data, features corresponding to the subset collection of feature sets to develop training data;
• applying the training data to a neural network to develop a learned model;
• performing validation of the learned model and iteratively repeating the performing of the evolutionary feature selection if the learned model does not meet a predefined stopping threshold;
• and
• deploying the learned model in a processing system configured to extract from an input signal features, corresponding to the subset collection of feature sets, to be processed by the neural network based on the learned model.