Abstract: A low overhead voice activity detection technique for a noise-canceling bioacoustic sensor consumes, as inputs, signals generated by a body microphone and an environment microphone and produces, as an output based on these inputs, an indication of whether voice activity is present. The technique applies a novel dual ANC configuration that produces, in addition to the normal noise reduction function, a signal composed of environmental sounds with body sounds attenuated and a signal composed of body sounds projected into the acoustic environment, primarily speech sounds. The technique then applies to these derived signals an algorithm based on the scientific observation that speech intensity, within limits, rises and falls to match environmental sound intensity to provide voice activity detection.

Abstract: A model-based method for assessing acoustic signal quality in a heart monitoring device. The personal heart sound interval distribution of a person being actively monitored is compared with a modeled global heart sound interval distribution shared by most human beings after which processing action is taken consistent with the quality assessment. The error in the best fit between the personal interval distribution and the global interval distribution is presumed to be caused predominantly by noise, allowing the quality of the fit to serve as a proxy for the level of noise in the acoustic signal and used in making processing decisions.

Abstract: A method and engine for identifying events in a sensor signal using rate-dependent feature sets couples trial event identifications made using different rate-dependent feature sets with rate-dependent quality assessments to overcome the mutual dependency of event attributes and the rate of events. The quality assessments compare event identifications made at different rates against quality benchmarks for event identifications at those rates, such as event stability benchmarks, to determine which feature sets are producing reliable event identifications and which are not. The invention enables electronic monitoring devices to make improved event identifications by accepting identifications made by presently reliable rate-dependent feature sets and rejecting those made by presently unreliable rate-dependent feature sets. In one application of the invention, the method and engine enable a respiration monitoring device to make improved respiration segment identifications.

Abstract: A low overhead voice activity detection technique for a noise-canceling bioacoustic sensor consumes, as inputs, signals generated by a body microphone and an environment microphone and produces, as an output based on these inputs, an indication of whether voice activity is present. The technique applies a novel dual ANC configuration that produces, in addition to the normal noise reduction function, a signal composed of environmental sounds with body sounds attenuated and a signal composed of body sounds projected into the acoustic environment, primarily speech sounds. The technique then applies to these derived signals an algorithm based on the scientific observation that speech intensity, within limits, rises and falls to match environmental sound intensity to provide voice activity detection.