Abstract: The invention enables determining at least one of an insurance risk score or an insurance cost through the steps of (i) receiving from one or more sensors disposed within one or more wearable devices worn by the individual, health parameter data corresponding to one or more states associated with the individual, (ii) validating the received health parameter data based on one or more predefined validation rules, (iii) responsive to validation of the health parameter data, recording said health parameter data and a unique ID associated with the individual in a blockchain, and (iv) retrieving from the blockchain, a plurality of instances of health parameter data associated with the unique ID, and generating based on an analysis of the retrieved plurality of instances of health parameter data, a risk score associated with the individual.

Abstract: A method for estimating a noise power level difference (NPLD) between a primary microphone and a reference microphone of an audio device includes obtaining primary and reference channels of an audio signal with primary and reference microphones of an audio device and estimating a noise magnitude of the reference channel of the audio signal to provide a noise variance estimate for one or more frequencies. A modelled probability density function (PDF) of a fast Fourier transform (FFT) coefficient of the primary channel of the audio signal is maximized to provide a NPLD between the noise variance estimate of the reference channel and a noise variance estimate of the primary channel. A modelled PDF of an FFT coefficient of the reference channel of the audio signal is maximized to provide a complex speech power level difference (SPLD) coefficient between the speech FFT coefficients of the primary and reference channel.

Abstract: A method for adjusting a degree of filtering applied to an audio signal includes modeling a probability density function (PDF) of a fast Fourier transform (FFT) coefficient of a primary channel and reference channel of the audio signal; maximizing at least one of PDFs to provide a discriminative relevance difference (DRD) between a noise magnitude estimate of the reference channel and a noise magnitude estimate of the primary channel. The method further includes emphasizing the primary channel when the spectral magnitude of the primary channel is stronger than the spectral magnitude of the reference channel; and deemphasizing the primary channel when the spectral magnitude of the reference channel is stronger than the spectral magnitude of the primary channel.

Abstract: A “running range normalization” method includes computing running estimates of the range of values of features useful for voice activity detection (VAD) and normalizing the features by mapping them to a desired range. Running range normalization includes computation of running estimates of the minimum and maximum values of VAD features and normalizing the feature values by mapping the original range to a desired range. Smoothing coefficients are optionally selected to directionally bias a rate of change of at least one of the running estimates of the minimum and maximum values. The normalized VAD feature parameters are used to train a machine learning algorithm to detect voice activity and to use the trained machine learning algorithm to isolate or enhance the speech component of the audio data.

Abstract: Embodiments disclosed herein extend to methods, systems, and computer program products for analyzing digital data. A source of digital data is analyzed and separated into segments, each segment having an identifiable characteristic. The separated segments are copied into planes of a higher dimension. The separated segments are compared to determine a resemblance factor. A fingerprint is generated for segments having a resemblance factor above a particular threshold. Based upon the generated fingerprint, a data source may be filtered to block or to pass data corresponding to the generated fingerprint. The digital data may be audio data, video data, or other data.