Abstract: Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most-likely predecessor state and a corresponding most-likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.

Abstract: A method for processing and iteratively enhancing and estimating a source audio signal received at two audio receivers is provided. In one embodiment, the method involves the use of codebook constrained iterative binaural Wiener filter (CCIBWF). The provided CCIBWF embodiment can improve the quality of speech received at two audio receivers both in terms of noise reduction and speech intelligibility. In one embodiment, optimum speech enhancement performance was achieved within two iterations of the CCIBWF scheme. Further, the embodiment of the CCIBWF scheme introduces minimal distortion to the binaural cues, such as the interaural time delay cues, thereby preserving localization information of the audio source. The embodiment of the CCIBWF is also able to relatively accurately track the Time Delay of Arrival (TDOA) when the audio source is moving. This ensures that the performance of the CCIBWF scheme is not significantly degraded due to the selection of wrong codebooks.

Abstract: Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most-likely predecessor state and a corresponding most-likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.