Methods related to Generalized Mutual Interdependence Analysis (GMIA), a low complexity statistical method for projecting data in a subspace that captures invariant properties of the data, are implemented on a processor based system. GMIA methods are applied to the signal processing problem of voice activity detection and classification. Real-world conversational speech data are modeled to fit the GMIA assumptions. Low complexity GMIA computations extract reliable features for classification of sound under noisy conditions and operate with small amounts of data. A speaker is characterized by a slow varying or invariant channel that is learned and is tracked from single channel data by GMIA methods.
