Starkey Laboratories, Inc.

Sonos, Inc.

Wenyu Jin

Tao Zhang

Henning Schepker

Patrick Kevin McPherson

Adib Mehrabi

Tim Schoof

Ivo Merks

Jingjing Xu

Masahiro Sunohara

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.

Playback device self-calibration using PCA-based room response estimation

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.

Multi-channel AEC system identification for self-calibration

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.

Method and apparatus for automatic correction of real ear measurements

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.

Apparatus and method for performing active occlusion cancellation with audio hear-through

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.

Apparatus and method for estimation of eardrum sound pressure based on secondary path measurement

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.

Hearing device with feedback instability detector that changes an adaptive filter

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.

Growing community of inventors

Eden Prairie, MN, United States of America

Wenyu Jin