Facebook Technologies, LLC

Meta Platforms Technologies, LLC

Oculus, Vr, LLC

Ravish Mehra

Antonio John Miller

Vladimir Tourbabin

Philip Robinson

Sean Jason Keller

David R Perek

Morteza Khaleghimeybodi

Riccardo DeSalvo

Tristan Thomas Trutna

Jacob Ryan Donley

Christoph Omid Hohnerlein

Elia Gatti

William Owen Brimijoin, Ii

Nils Thomas Fritiof Lunner

Robert Y Wang

Christopher David Twigg

Elliot Saba

David Lou Alon

Maria Cuevas Rodriguez

Scott Phillips Porter

Peter Harty Dodds

Matthew Robert Schwab

Dustin Jeffery Gordon Krogstad

Ian Andreas Marquez

Pablo Francisco Faundez Hoffman

Sabrina Monique Sandoval

Cleveland-Joel Chavez Wilcox

A system for presenting audio content to a user. The system comprises one or more microphones coupled to a frame of a headset. The one or more microphones capture sound from a local area. The system further comprises an audio controller integrated into the headset and communicatively coupled to an in-ear device worn by a user. The audio controller identifies one or more sound sources in the local area based on the captured sound. The audio controller further determines a target sound source of the one or more sound sources and determines one or more filters to apply to a sound signal associated with the target sound source in the captured sound. The audio controller further generates an augmented sound signal by applying the one or more filters to the sound signal and provides the augmented sound signal to the in-ear device for presentation to a user.

Audio system for suppressing leakage signal from in-ear device

Audio system using individualized sound profiles

An audio system for providing content to a user. The system includes a first and a second transducer assembly of a plurality of transducer assemblies, an acoustic sensor, and a controller. The first transducer assembly couples to a portion of an auricle of the user's ear and vibrates over a first range of frequencies based on a first set of audio instructions. The vibration causes the portion of the ear to create a first range of acoustic pressure waves. The second transducer assembly is configured to vibrate over a second range of frequencies to produce a second range of acoustic pressure waves based on a second set of audio instructions. The acoustic sensor detects acoustic pressure waves at an entrance of the ear. The controller generates the audio instructions based on audio content to be provided to the user and the detected acoustic pressure waves from the acoustic sensor.

Hybrid audio system for eyewear devices

The disclosed computer-implemented method may include receiving a signal for each channel of an audio transducer array on a wearable device. The method may also include calculating a beamformed signal for each beam direction of a set of beamforming filters for the wearable device. Additionally, the method may include classifying a first beamformed signal from the calculated beamformed signals into a first class of sound and a second beamformed signal from the calculated beamformed signals into a second class of sound. The method may also include adjusting, based on the classifying, a gain of the first beamformed signal relative to the second beamformed signal. Furthermore, the method may include converting the beamformed signals into spatialized binaural audio based on a position of a user. Finally, the method may include transmitting the spatialized binaural audio to a playback device. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for classifying beamformed signals for binaural audio playback

An eyewear device includes an audio system. In one embodiment, the audio system includes a microphone array that includes a plurality of acoustic sensors. Each acoustic sensor is configured to detect sounds within a local area surrounding the microphone array. For a plurality of the detected sounds, the audio system performs a direction of arrival (DoA) estimation. Based on parameters of the detected sound and/or the DoA estimation, the audio system may then generate or update one or more acoustic transfer functions unique to a user. The audio system may use the one or more acoustic transfer functions to generate audio content for the user.

Audio system for dynamic determination of personalized acoustic transfer functions

An audio system for a wearable device dynamically updates acoustic transfer functions. The audio system is configured to estimate a direction of arrival (DoA) of each sound source detected by a microphone array relative to a position of the wearable device within a local area. The audio system may track the movement of each sound source. The audio system may form a beam in the direction of each sound source. The audio system may identify and classify each sound source based on the sound source properties. Based on the DoA estimates, the movement tracking, and the beamforming, the audio system generates or updates the acoustic transfer functions for the sound sources.

Acoustic transfer function personalization using sound scene analysis and beamforming

A method for manufacturing a cartilage conduction audio device is disclosed. A manufacturing system receives data describing a three-dimensional shape of an ear (e.g., the outer ear, behind the ear, the concha bowel, etc.) of a user. The system identifies one or more locations for one or more transducers along a back of an auricle of the ear for the user that vibrate the auricle over a frequency range causing the auricle to create an acoustic pressure wave at an entrance of the ear canal. The system then generates a design for a cartilage conduction audio device for the user based on the one or more identified locations of the transducers at which acoustic pressure waves generated by the one or more transducers satisfy a threshold performance metric for the user. The design may then be used to fabricate the cartilage conduction audio device.

Manufacturing a cartilage conduction audio device

An audio system captures audio data of test sounds through a microphone of a headset worn by a user. The test sounds are played by an external speaker, and the audio data includes audio data captured for different orientations of the headset with respect to the external speaker. A set of head-related transfer function (HRTFs) is calculated based at least in part on the audio data of the test sounds at the different orientations of the headset. A portion of the set of HRTFs is discarded to create an intermediate set of HRTFs. The discarded portion corresponding to one or more distortion regions that are based in part on wearing the headset. One or more HRTFs are generated that correspond to the discarded portion using at least some of the intermediate set of HRTFs to create an individualized set of HRTFs for the user.

Compensating for effects of headset on head related transfer functions

Embodiments relate to a headset that filters sounds according to a direction of a gaze of a user wearing the headset. The user wears the headset including an eye tracking unit and one or more microphones. The eye tracking unit tracks an orientation of an eye of the user to determine the direction of the gaze of the user. The direction of the gaze may be different from a facing direction of the headset. According to the determined direction of the gaze of the user, input sound signals generated by the microphones can be beamformed to amplify or emphasize sound originating from the direction of the gaze.

Gaze-directed audio enhancement

A head-mounted display (HMD) tracks a user's hand positions, orientations, and gestures using an ultrasound sensor coupled to the HMD. The ultrasound sensor emits ultrasound signals that reflect off the hands of the user, even if a hand of the user is obstructed by the other hand. The ultrasound sensor identifies features used to train a machine learning model based on detecting reflected ultrasound signals. For example, one of the features is the time delay between consecutive reflected ultrasound signals detected by the ultrasound sensor. The machine learning model learns to determine poses and gestures of the user's hands. The HMD optionally includes a camera that generates image data of the user's hands. The image data can also be used to train the machine learning model. The HMD may perform a calibration process to avoid detecting other objects and surfaces such as a wall next to the user.

Hand tracking using an ultrasound sensor on a head-mounted display

One embodiment of the present application sets forth a computer-implemented method that includes receiving, from a first microphone, a first input acoustic signal, generating a first audio spectrum from at least the first input acoustic signal, wherein the first audio spectrum includes a set of time-frequency bins, and selecting a first time-frequency bin from the set based on a first local space-domain distance (LSDD) computed for the first time-frequency bin.

Techniques for selecting a direct path acoustic signal

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Redmond, WA, United States of America

Ravish Mehra