Abstract: An exemplary system includes a memory storing instructions and one or more processors communicatively coupled to the memory and configured to execute the instructions to perform a process comprising: accessing fitting data representative of fitting parameters set by a fitting application to fit a hearing device to a user; determining user behavior data indicative of a hearing intention of the user in an auditory scene where the user is located; determining auditory scene data representative of information about the auditory scene; and implementing, based on the fitting data, the user behavior data, and the auditory scene data, a sound processing delay scheme for use by the hearing device.

Abstract: A method for determining social interaction of a user wearing a hearing device which comprises at least one microphone and at least one classifier. The method comprises: receiving an audio signal from the at least one microphone and/or a sensor signal from the at least one further sensor; identifying, by the at least one classifier, one or more predetermined user activity values by evaluating the audio signal from the at least one microphone and/or the sensor signal from the at least one further sensor; and calculating a user social interaction metric indicative of the social interaction of the user from the identified user activity values, wherein the user activity values are assigned to predefined social interaction levels, and wherein the user social interaction metric is a function of the user activity values weighted with their respective contribution to each of the social interaction levels.

Abstract: A method is described for tracking happy moments of a user wearing a hearing device comprising at least one microphone and at least one classifier configured to detect specific states of the user or of the user's environment based on a sensor signal indicative of the user's mood.

Abstract: A method for determining social interaction of a user wearing a hearing device which comprises at least one microphone and at least one classifier. The method comprises: receiving an audio signal from the at least one microphone and/or a sensor signal from the at least one further sensor; identifying, by the at least one classifier, one or more predetermined user activity values by evaluating the audio signal from the at least one microphone and/or the sensor signal from the at least one further sensor; and calculating a user social interaction metric indicative of the social interaction of the user from the identified user activity values, wherein the user activity values are assigned to predefined social interaction levels, and wherein the user social interaction metric is a function of the user activity values weighted with their respective contribution to each of the social interaction levels.

Abstract: The disclosed technology generally relates to a hearing device configured to process a signal in a first path and a second path, where the first path and second path apply different digital signal processing operations to the signal. The hearing device is configured to determine a relatedness factor that compares the processed signals along the first and second path. Based on the relatedness factor, the hearing device can apply different gains to the signal in the first and second paths. The hearing device can output a combined signal based on the signals from the first and second paths. In some embodiments, the second path can be associated with receiving a signal from an external device.

Abstract: The disclosed technology generally relates to a hearing device configured to process a signal in a first path and a second path, where the first path and second path apply different digital signal processing operations to the signal. The hearing device is configured to determine a relatedness factor that compares the processed signals along the first and second path. Based on the relatedness factor, the hearing device can apply different gains to the signal in the first and second paths. The hearing device can output a combined signal based on the signals from the first and second paths. In some embodiments, the second path can be associated with receiving a signal from an external device.

Abstract: An exemplary hearing device is configured to receive a first signal output by a local microphone that is a part of the hearing device at a first location. The first signal is representative of a degraded version of audio content as detected by the local microphone. The audio content is provided by an audio source at a second location. The hearing device is further configured to receive a second signal from a remote microphone system located in proximity of the second location. The second signal is representative of a cleaner version of the audio content as detected by the remote microphone system. The hearing device is further configured to determine a relatedness factor between the first signal and the second signal and apply, based on the relatedness factor, a signal enhancing operation to the first signal to output an enhanced first signal.

Abstract: In some implementations, the disclosed technology determines that a microphone is likely closer to a sound source compared to other microphones partially based on determining a spectral standard deviation associated with an audio signal captured by the microphone. In some implementations, the disclosed technology determines a spectral standard deviation of audio signals associated with different microphones and determines that the microphone with the lowest spectral standard deviation is likely the closest microphone to a sound source. The disclosed technology can determine the closest microphone generally independent of input signal level, independent of microphone sensitivity, independent of microphone design, independent of microphone manufacturer, and independent of the sound source intensity (e.g., “loud” or “soft”).

Abstract: In some implementations, the disclosed technology determines that a microphone is likely closer to a sound source compared to other microphones partially based on determining a spectral standard deviation associated with an audio signal captured by the microphone. In some implementations, the disclosed technology determines a spectral standard deviation of audio signals associated with different microphones and determines that the microphone with the lowest spectral standard deviation is likely the closest microphone to a sound source. The disclosed technology can determine the closest microphone generally independent of input signal level, independent of microphone sensitivity, independent of microphone design, independent of microphone manufacturer, and independent of the sound source intensity (e.g., “loud” or “soft”).

Abstract: An exemplary hearing device is configured to receive a first signal output by a local microphone that is a part of the hearing device at a first location. The first signal is representative of a degraded version of audio content as detected by the local microphone. The audio content is provided by an audio source at a second location. The hearing device is further configured to receive a second signal from a remote microphone system located in proximity of the second location. The second signal is representative of a cleaner version of the audio content as detected by the remote microphone system. The hearing device is further configured to determine a relatedness factor between the first signal and the second signal and apply, based on the relatedness factor, a signal enhancing operation to the first signal to output an enhanced first signal.