Apple Inc.

Knowles Electronics, Inc.

John Woodruff

Stephen Olivier Lemay

Taylor Gerard Carrigan

Tony S Verma

Carlos M Avendano

Jing Xia

Yacine Azmi

Ian M Fisch

Tom-Davy W Saux

Mehrez Souden

Tyrone T Chen

Michael M Goodwin

Vasu Iyengar

Yang Lu

Andrew E Greenwood

Shailesh Sakri

Jonathon Ray

Pratik Shah

Sai Ravi Teja Pulugurtha

Andreas Koutrouvelis

Elizabeth Caroline Cranfill

Pedro Mari

Nathan De Vries

Jonathan J Huang

Vladan Bajic

Nicholas D Felton

Aroon Pahwa

Jakub Mazur

Harsha Rao

Joshua D Atkins

Eamon Francis Gilravi

Symeon Delikaris Manias

David Klein

Christopher Wilson

Ante Jukic

Ruchi Goswami

Murali Mohan Deshpande

Sridhar Krishna Nemala

Frederic Caldwell

Sharon Gadonniex

Malakapati Loka Nagendra Prasad

Hindupur Keerthi Sagar

Rohit Paturi

Eric H Zhang

Ramsey Deeb Abouzahra

Andriy Suden

David Harrison

Joe Williams

Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.

Audio source separation for audio devices

An audio processing device may generate a plurality of microphone signals from a plurality of microphones of the audio processing device. The audio processing device may determine a gaze of a user who is wearing a playback device that is separate from the audio processing device, the gaze of the user being determined relative to the audio processing device. The audio processing device may extract speech that correlates to the gaze of the user, from the plurality of microphone signals of the audio processing device by applying the plurality of microphone signals of the audio processing device and the gaze of the user to a machine learning model. The extracted speech may be played to the user through the playback device.

Machine learning and user driven selective hearing

A media system and a method of using the media system to accommodate hearing loss of a user, are described. The method includes selecting a personal level-and-frequency dependent audio filter that corresponds to a hearing loss profile of the user. The personal level-and-frequency dependent audio filter can be one of several level-and-frequency-dependent audio filters having respective average gain levels and respective gain contours. An accommodative audio output signal can be generated by applying the personal level-and-frequency dependent audio filter to an audio input signal to enhance the audio input signal based on an input level and an input frequency of the audio input signal. The audio output signal can be played by an audio output device to deliver speech or music that the user perceives clearly, despite the hearing loss of the user. Other aspects are also described and claimed.

Media system and method of accommodating hearing loss using a personalized audio filter

Signal to noise ratio, SNR, is determined in an acoustic ambient environment of an against-the-ear audio device worn by a user, wherein the acoustic ambient environment contains speech by a talker. When the SNR is above a threshold, dynamic range control is applied, as positive gain versus input level, to an audio signal from one or more microphones of the audio device. When the SNR is below the threshold, the dynamic range control applies as zero gain or negative gain to the audio signal. Other aspects are also described and claimed.

Ambient noise aware dynamic range control and variable latency for hearing personalization

Media system and method of amplifying audio signal using audio filter corresponding to hearing loss profile

Signal energy in auditory sub-bands of an input audio signal is determined. Sound pressure level, SPL, in those sub-bands is then determined, based on the signal energy and based on sound output sensitivity of the against-the-ear audio device. In one instance, at least first and second gain lookup tables are determined based on a hearing profile of a user of the against-the-ear audio device. Sub-band gains that are to be applied to the input audio signal are determined based on the determined SPL. When the input audio signal is for example telephony the sub-band gains are computed using the first gain lookup table, and when the input audio signal is for example media the sub-band gains are computed using the second gain lookup table. Other aspects are also described and claimed.

Audio signal processing for sound compensation

A system and method provides for multibeam keyword detection. A composite audio signal may include sound components. The system and method groups the sound components into subsets based on the angles of arrival of sound components. Keyword detectors evaluate each subset and determine whether a keyword is present.

Multibeam keyword detection system and method

An audio system has an ambient sound enhancement function, in which an against-the-ear audio device having a speaker converts a digitally processed version of an input audio signal into sound. The audio system also has an acoustic noise cancellation (ANC) function that may be combined in various ways with the sound enhancement function, and that may be responsive to voice activity detection. Other aspects are also described and claimed.

Ambient sound enhancement and acoustic noise cancellation based on context

Media system and method of accommodating hearing loss

The present disclosure generally relates to user interfaces and techniques for managing audio exposure using a computer system (e.g., an electronic device). In accordance with some embodiments, the electronic device displays a graphical indication of a noise exposure level over a first period of time with an area of the graphical indication that is colored to represent the noise exposure level, the color of the area transitioning from a first color to a second color when the noise exposure level exceeds a first threshold. In accordance with some embodiments, the electronic device displays noise exposure levels attributable to a first output device type and a second output device type and, in response to selecting a filtering affordance, visually distinguishes a set of noise exposure levels attributable to the second output device type.

User interfaces for managing audio exposure

An audio system has an ambient sound enhancement function, in which an against-the-ear audio device having a speaker converts a digitally processed version of an input audio signal into sound. The input audio signal may be amplified where the amplification may be in accordance with a stored hearing profile of the user (personalized ambient sound enhancement.) The audio system also has an acoustic noise cancellation (ANC) function that may be combined in various ways with the sound enhancement function, and that may be responsive to voice activity detection. Other aspects are also described and claimed.

Ambient sound enhancement based on hearing profile and acoustic noise cancellation

A method for estimating directions of arrival of audio signals based on categories is disclosed herein. The method includes receiving an audio signal; generating a plurality of filters based on the audio signal, each of the filters corresponding to one of a plurality of sound content categories; separating the audio signal into a plurality of content-based audio signals by applying the filters to the audio signal, each of the content-based audio signals contains a content of a corresponding sound content category among the plurality of sound content categories; for each of the content-based audio signals, generating a direction of arrival (DOA) for a sound source of the content-based audio signal.

Direction of arrival estimation for multiple audio content streams

Systems and methods for stereo separation and directional suppression are provided. An example method includes receiving a first audio signal, representing sound captured by a first microphone associated with a first location, and a second audio signal, representing sound captured by a second microphone associated with a second location. The microphones comprise omni-directional microphones. The distance between the first and second microphones is limited by the size of a mobile device. A first channel signal of a stereo signal is generated by forming, based on the first and second audio signals, a first beam at the first location. A second channel signal of the stereo signal is generated by forming, based on the first and second audio signals, a second beam at the second location. First and second directions, associated respectively with the first and second beams, are fixed relative to a line between the first and second locations.

Stereo separation and directional suppression with omni-directional microphones

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Santa Cruz, CA, United States of America

John Woodruff