Abstract: Adaptive occlusion cancellation is performed in an ear-wearable device using an adaptive filter. An adaptive gain of the adaptive filter is used to determine a leakage path estimate between an external source and an eardrum of the user through the ear-wearable device. The leakage path estimate is used to update an adaptive hear-through filter of the ear-wearable device. The updated adaptive hear-through filter is used for hear-through processing in the ear-wearable device.

Abstract: An individual cutoff frequency for an individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using an individual secondary path measurement, a reduced dimensionality estimate, and a least squares estimate. A sound pressure level at an eardrum of the user is predicted using the first and second receiver-to-eardrum responses.

Abstract: An adaptive feedback canceller of an ear-wearable device has an adaptive foreground filter that inserts a feedback cancellation signal into a digitized input signal to produce an error signal. An instability detector of the device is configured to extract wo or more features from the error signal. The instability detector has a machine learning module that determines instability in the error signal based on the two or more features. The instability module changes the adaptive foreground filter in response to determining the instability. The change causes the adaptive foreground filter to have a faster adaptation to perturbations in the error signal compared to a previously used step size.

Abstract: Disclosed herein are systems and methods for automatic correction of real ear measurements (REMs). A sound signal is produced through a receiver of a hearing device, and a sound pressure signal is sensed using a microphone placed inside the ear canal. The sound pressure signal is transformed to obtain a frequency response signal, a local minimum of the frequency response signal is detected above a programmable frequency level, and a spectral flatness of the frequency response signal is calculated in a selected frequency band surrounding the local minimum. If the spectral flatness is greater than a selected threshold value, acoustic correction is applied to the frequency response in the selected frequency band using an estimated transfer function to obtain a corrected sound pressure frequency response. The corrected sound pressure frequency response is used to modify, or make a recommendation to modify, a physical or operational characteristic of the hearing device.

Abstract: Disclosed herein are systems and methods for automatic correction of real ear measurements (REMs). A sound signal is produced through a receiver of a hearing device, and a sound pressure signal is sensed using a microphone placed inside the ear canal. The sound pressure signal is transformed to obtain a frequency response signal, a local minimum of the frequency response signal is detected above a programmable frequency level, and a spectral flatness of the frequency response signal is calculated in a selected frequency band surrounding the local minimum. If the spectral flatness is greater than a selected threshold value, acoustic correction is applied to the frequency response in the selected frequency band using an estimated transfer function to obtain a corrected sound pressure frequency response. The corrected sound pressure frequency response is used to modify, or make a recommendation to modify, a physical or operational characteristic of the hearing device.

Abstract: An individual cutoff frequency for an individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using an individual secondary path measurement, a reduced dimensionality estimate, and a least squares estimate. A sound pressure level at an eardrum of the user is predicted using the first and second receiver-to-eardrum responses.

Abstract: Examples described herein relate to calibration of audio playback devices in a media playback system using inputs derived from a multi-channel adaptive filter of an acoustic echo canceller. Example playback devices described herein may utilize one or more techniques for calibration, which may be implemented as various calibration procedures. In some implementations, an example playback device may implement a self-calibration procedure, which involves the playback device calibrating (or re-calibrating) itself during operation. Yet further, the playback device may estimate acoustic impulse responses from a multi-channel adaptive filter of an acoustic echo canceller to use as inputs to the self-calibration procedure.

Abstract: Example techniques relate to audio playback device calibration. Example playback devices described herein may utilize one or more techniques for calibration, which may be implemented as various calibration procedures. In some implementations, the example media playback system may support multiple types of calibration, perhaps with different calibration procedures being used for different types of playback devices (e.g., with different capabilities) or in different situations (e.g., with or without user involvement).

Abstract: Secondary path measurements and associated acoustic transducer-to-eardrum responses are obtained from test subjects. Both a least squares estimate and a reduced dimensionality estimate are determined that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses. An individual secondary path measurement for a user is performed based on a test signal transmitted via a hearing device into an ear canal of the user. An individual cutoff frequency for the individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using the individual secondary path measurement and the least squares estimate. A sound pressure level at an eardrum of the user can be predicted using the first and second receiver-to-eardrum responses.

Abstract: Example techniques described herein relate to calibration of a playback device. Using example techniques, a playback device may self-calibrate by measuring a self-response and using a transfer function to map the self-response to an estimate of a room response. The playback device may determine calibration settings to at least partially offset acoustic characteristics of an environment surrounding the playback device as represented in the estimated room response. The transfer function may be derived from a principal component analysis of a dataset including multiple room responses, perhaps as measured during a multi-point calibration process of the playback device in various environments and conditions.

Abstract: An ear-wearable device is operable to receive a reference signal from outside an ear canal of a user and an error signal from inside of the ear canal. A physical propagation path between the outside and inside of the ear canal defines a primary path, and amplified sound produced inside of the ear canal propagates over a secondary path to combine with direct noise at the ear canal. A noise signal inside the ear canal is estimated from the reference signal based on estimate of the primary and secondary paths. The estimated noise signal and the error signal are used to produce coefficients of an adaptive filter. The adaptive filter is used to produce an anti-noise signal, which is used actively cancel noise in the ear canal.

Abstract: Secondary path measurements and associated acoustic transducer-to-eardrum responses are obtained from test subjects. Both a least squares estimate and a reduced dimensionality estimate are determined that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses. An individual secondary path measurement for a user is performed based on a test signal transmitted via a hearing device into an ear canal of the user. An individual cutoff frequency for the individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using the individual secondary path measurement and the least squares estimate. A sound pressure level at an eardrum of the user can be predicted using the first and second receiver-to-eardrum responses.

Abstract: An ear-wearable electronic device is operable to apply a low-pass filter to the digitized voice signal to remove a high-frequency component and obtain a low-frequency component. Speech enhancement is applied to the low-frequency component. Blind bandwidth extension is applied to the enhanced low-frequency component to recover or synthesize an estimate of at least part of the high frequency component. An enhanced speech signal is output that is a combination of the enhanced low-frequency component and the bandwidth-extended high frequency component.

Abstract: An adaptive feedback canceller of an ear-wearable device has an adaptive foreground filter that inserts a feedback cancellation signal into a digitized input signal to produce an error signal. An instability detector of the device is configured to extract wo or more features from the error signal. The instability detector has a machine learning module that determines instability in the error signal based on the two or more features. The instability module changes the adaptive foreground filter in response to determining the instability. The change causes the adaptive foreground filter to have a faster adaptation to perturbations in the error signal compared to a previously used step size.

Abstract: Adaptive occlusion cancellation is performed in an ear-wearable device using an adaptive filter. An adaptive gain of the adaptive filter is used to determine a leakage path estimate between an external source and an eardrum of the user through the ear-wearable device. The leakage path estimate is used to update an adaptive hear-through filter of the ear-wearable device. The updated adaptive hear-through filter is used for hear-through processing in the ear-wearable device.

Abstract: Secondary path measurements and associated acoustic transducer-to-eardrum responses are obtained from test subjects. Both a least squares estimate and a reduced dimensionality estimate are determined that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses. An individual secondary path measurement for a user is performed based on a test signal transmitted via a hearing device into an ear canal of the user. An individual cutoff frequency for the individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using the individual secondary path measurement and the least squares estimate. A sound pressure level at an eardrum of the user can be predicted using the first and second receiver-to-eardrum responses.

Abstract: Disclosed herein are systems and methods for automatic correction of real ear measurements (REMs). A sound signal is produced through a receiver of a hearing device, and a sound pressure signal is sensed using a microphone placed inside the ear canal. The sound pressure signal is transformed to obtain a frequency response signal, a local minimum of the frequency response signal is detected above a programmable frequency level, and a spectral flatness of the frequency response signal is calculated in a selected frequency band surrounding the local minimum. If the spectral flatness is greater than a selected threshold value, acoustic correction is applied to the frequency response in the selected frequency band using an estimated transfer function to obtain a corrected sound pressure frequency response. The corrected sound pressure frequency response is used to modify, or make a recommendation to modify, a physical or operational characteristic of the hearing device.

Abstract: A device estimates direction of arrival (DOA) of sound from sound sources received by P microphones, wherein P?>1. The device is configured to transform the output signals of the microphones into the frequency domain and compute a covariance matrix for each of N frequency bins in a range of frequencies of the sound. Further, the device is configured to calculate an adapted covariance matrix from each of the covariance matrices for wide-band merging, calculate an accumulated covariance matrix from the N adapted covariance matrices, and estimate the DOA for each of the sound sources based on the accumulated covariance matrix. In order to calculate an adapted covariance matrix from a covariance matrix, the device is configured to spectrally decompose the covariance matrix and obtain a plurality of eigenvectors, rotate each obtained eigenvector, and construct each rotated eigenvector back to the shape of the covariance matrix.

Abstract: Secondary path measurements and associated acoustic transducer-to-eardrum responses are obtained from test subjects. Both a least squares estimate and a reduced dimensionality estimate are determined that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses. An individual secondary path measurement for a user is performed based on a test signal transmitted via a hearing device into an ear canal of the user. An individual cutoff frequency for the individual secondary path measurement is determined. First and second acoustic transducer-to-eardrum responses below and above the cutoff frequency are determined using the individual secondary path measurement and the least squares estimate. A sound pressure level at an eardrum of the user can be predicted using the first and second receiver-to-eardrum responses.

Abstract: A sound processing node is provided for an arrangement of sound processing nodes configured to receive a plurality of sound signals. The sound processing node comprises a processor configured to generate an output signal based on the plurality of sound signals weighted by a plurality of beamforming weights. The processor is configured to adaptively determine the plurality of beamforming weights on the basis of an adaptive linearly constrained minimum variance beamformer using a transformed version of a least mean squares formulation of a constrained gradient descent approach. The transformed version of the least mean squares formulation of the constrained gradient descent approach is based on a transformation of the least mean squares formulation of the constrained gradient descent approach to the dual domain.