Abstract: An incident clustering system for detecting clusters among incident reports can include a data intake module configured to receive incident text strings associated with respective incident reports input by a user, a pre-processing module operatively connected to the data intake module to receive the incident text strings from the data intake module and to pre-process the incident text strings to output pre-processed text strings associated with the respective incident reports, and a token module operatively connected to the pre-processing module to receive the pre-processed text strings. The token module can be configured to identify one or more phrases-of-interest having a plurality of words in the pre-processed text strings and concatenate the plurality of words of each of one or more phrases-of-interest to output concatenated tokens associated with the respective incident reports.

Abstract: A computer implemented method and system for detecting one or more anomaly conditions in one or more computer devices. A Machine Learning (ML) model is trained for each of the one or more computer devices to determine threshold operating values for time-based metric data associated with each of the one or more computer devices. Utilizing the trained ML model, time-based metric data is compared for each of the one or more computer devices to the determined threshold operating values to determine if the time-based metric data falls outside of the determined threshold operating values. Provided is notification of an anomaly condition for a computer device responsive to determining the time-based metric data falls outside of the determined threshold operating values associated with the computer device.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to perform ANR on the audio stream according to a set of parameters in the first DSP. The platform includes a second DSP processor configured to detect at least one of a sound pressure level (SPL) or a performance characteristic, and includes a general purpose (GP) processor operationally coupled to the first DSP processor and the second DSP processor and configured to achieve a desired user experience in response to at least one of the SPL or the performance characteristic detected by the second DSP deviating from a corresponding threshold or rule.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to characterize a malfunction in response to instability or error condition events detected by the second DSP.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR). In some cases, a method for processing audio signals includes receiving a noise reduction signal configured to modify an audio signal and generate a noise reduced audio signal, the noise reduction signal having a nominal loop gain; and generating an adjusted noise reduction signal in response to a detected adverse low frequency event, the adjusted noise reduction signal having a modulated loop gain configured to reduce artifacts in the noise reduced audio signal.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR). In some cases, an ANR system for a wearable audio device includes: a fixed filter that receives a signal from a feedback microphone and outputs a noise reduction signal, where the fixed filter is configured to provide ANR with a nominal loop gain; and a tunable filter that outputs an adjusted noise reduction signal by modulating the nominal loop gain in response to low frequency noise being detected in the noise reduction signal, where modulating the nominal loop gain includes reducing low frequency ANR performance.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to characterize a malfunction in response to instability or error condition events detected by the second DSP.

Abstract: Various aspects include active noise reduction (ANR) devices and approaches, one approach including: receiving an input signal representing audio captured by a feedforward microphone of an ANR headphone; receiving an error signal representing audio captured by an error measurement sensor; generating an anti-noise signal configured to reduce a noise signal over a frequency range; and applying a gain to at least one of the input signal or the anti-noise signal over the frequency range based on the error signal, wherein the applied gain is configured to enhance noise reduction for a plurality of users having distinct fits for the ANR headphone.

Abstract: Various implementations include a method for implementing a computational architecture for a personal active noise reduction (ANR) device. A method includes receiving a source audio stream with a first DSP and performing ANR on the source audio stream utilizing operational parameters stored in the first DSP; outputting a processed audio stream from the first DSP; generating state data with a second DSP in response to an analysis of at least one of the source audio stream, microphone inputs and the processed audio stream, and communicating signals to the first DSP over a common bus coupled to the first and second DSPs to alter the operational parameters in the first DSP; and utilizing a general purpose processor coupled to both the first DSP and the second DSP to communicate control signals with a communication interface, process state data from the second DSP, and alter the operational parameters in the first DSP.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR). In some cases, an ANR system for a wearable audio device includes: a fixed filter that receives a signal from a feedback microphone and outputs a noise reduction signal, where the fixed filter is configured to provide ANR with a nominal loop gain; and a tunable filter that outputs an adjusted noise reduction signal by modulating the nominal loop gain in response to low frequency noise being detected in the noise reduction signal, where modulating the nominal loop gain includes reducing low frequency ANR performance.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR), where the ANR device includes: a feedback microphone; an electroacoustic transducer; and a feedback compensator configured to output a noise reduction signal to the electroacoustic transducer in response to a feedback signal from the feedback microphone, wherein the feedback compensator includes a tunable filter that modulates a loop gain in response to an adverse low frequency event being detected in the noise reduction signal outputted from the tunable filter, wherein the tunable filter is configured to maintain a substantially similar loop gain shape near a low frequency cross-over as the low frequency cross-over changes during loop gain modulation.

Abstract: Various aspects include active noise reduction (ANR) devices and approaches, one approach including: receiving an input signal representing audio captured by a feedforward microphone of an ANR headphone; receiving an error signal representing audio captured by an error measurement sensor; generating an anti-noise signal configured to reduce a noise signal over a frequency range; and applying a gain to at least one of the input signal or the anti-noise signal over the frequency range based on the error signal, wherein the applied gain is configured to enhance noise reduction for a plurality of users having distinct fits for the ANR headphone.

Abstract: Various aspects include active noise reduction (ANR) devices and approaches, one approach including: receiving an input signal representing audio captured by a feedforward microphone of an ANR headphone; receiving an error signal representing audio captured by an error measurement sensor; generating an anti-noise signal configured to reduce a noise signal over a frequency range; and applying a gain to at least one of the input signal or the anti-noise signal over the frequency range based on the error signal, where the gain is calculated by: filtering the anti-noise signal over the frequency range to generate a filtered feedforward signal, and filtering the error signal over the frequency range to generate a filtered error signal; estimating a feedforward path contribution to the error signal; and determining the gain based on a correlation between the filtered error signal and the filtered feedforward signal with the assigned feedforward path contribution to the error signal.

Abstract: Various aspects include active noise reduction (ANR) devices and approaches, one approach including: receiving an input signal representing audio captured by a feedforward microphone of an ANR headphone; receiving an error signal representing audio captured by an error measurement sensor; generating an anti-noise signal configured to reduce a noise signal over a frequency range; and applying a gain to at least one of the input signal or the anti-noise signal over the frequency range based on the error signal, where the gain is calculated by: filtering the anti-noise signal over the frequency range to generate a filtered feedforward signal, and filtering the error signal over the frequency range to generate a filtered error signal; estimating a feedforward path contribution to the error signal; and determining the gain based on a correlation between the filtered error signal and the filtered feedforward signal with the assigned feedforward path contribution to the error signal.

Abstract: Various implementations include a method for implementing a computational architecture for a personal active noise reduction (ANR) device. A method includes receiving a source audio stream with a first DSP and performing ANR on the source audio stream utilizing operational parameters stored in the first DSP; outputting a processed audio stream from the first DSP; generating state data with a second DSP in response to an analysis of at least one of the source audio stream, microphone inputs and the processed audio stream, and communicating signals to the first DSP over a common bus coupled to the first and second DSPs to alter the operational parameters in the first DSP; and utilizing a general purpose processor coupled to both the first DSP and the second DSP to communicate control signals with a communication interface, process state data from the second DSP, and alter the operational parameters in the first DSP.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to: communicate control signals with the communication interface, process state data from the second DSP, and alter the parameters on the first DSP.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR), where the ANR device includes: a feedback microphone; an electroacoustic transducer; and a feedback compensator configured to output a noise reduction signal to the electroacoustic transducer in response to a feedback signal from the feedback microphone, wherein the feedback compensator includes a tunable filter that modulates a loop gain in response to an adverse low frequency event being detected in the noise reduction signal outputted from the tunable filter, wherein the tunable filter is configured to maintain a substantially similar loop gain shape near a low frequency cross-over as the low frequency cross-over changes during loop gain modulation.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR), where the ANR device includes: a feedback microphone; an electroacoustic transducer; and a feedback compensator configured to output a noise reduction signal to the electroacoustic transducer in response to a feedback signal from the feedback microphone, wherein the feedback compensator includes a tunable filter that modulates a loop gain in response to an adverse low frequency event being detected in the noise reduction signal outputted from the tunable filter, wherein the tunable filter is configured to maintain a substantially similar loop gain shape near a low frequency cross-over as the low frequency cross-over changes during loop gain modulation.

Abstract: Various aspects include a wearable audio device having active noise reduction (ANR), where the ANR device includes: a feedback microphone; an electroacoustic transducer; and a feedback compensator configured to output a noise reduction signal to the electroacoustic transducer in response to a feedback signal from the feedback microphone, wherein the feedback compensator includes a tunable filter that modulates a loop gain in response to an adverse low frequency event being detected in the noise reduction signal outputted from the tunable filter, wherein the tunable filter is configured to maintain a substantially similar loop gain shape near a low frequency cross-over as the low frequency cross-over changes during loop gain modulation.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to: communicate control signals with the communication interface, process state data from the second DSP, and alter the parameters on the first DSP.