Utah State University

Todd K Moon

Jacob H Gunther

For canceling acoustic echoing, a processor receives audio signals comprising a speaker output and an ambient input. The processor further calculates separated output signals from mixed signals using a separating transfer function. The processor calculates a criterion function based on the separated output signals. In addition, the processor calculates an acoustic echo transfer function based on maximizing the a criterion function. The processor separates a source signal from the audio signal using the acoustic echo transfer function.

Echo cancelation using convolutive blind source separation

For calculating statistical Markov model-like state transition probabilities, a method represents state transition probabilities between a plurality of statistical Markov model-like states and output probabilities associated with a plurality of previous statistical Markov model-like states. The state transition probabilities between the plurality of previous states depend on a sequence of previous states of the plurality of previous states. The output probabilities associated with each of the plurality of states depend on the sequence of previous states.

Statistical model for systems incorporating history information

Environmental recognition systems may improve recognition accuracy by leveraging local and nonlocal features in a recognition target. A local decoder may be used to analyze local features, and a nonlocal decoder may be used to analyze nonlocal features. Local and nonlocal estimates may then be exchanged to improve the accuracy of the local and nonlocal decoders. Additional iterations of analysis and exchange may be performed until a predetermined threshold is reached. In some embodiments, the system may comprise extrinsic information extractors to prevent positive feedback loops from causing the system to adhere to erroneous previous decisions.

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Providence, UT, United States of America

Todd K Moon