Qualcomm Incorporated

Cambridge Silicon Radio Limited

Clarity Technologies, Inc.

Csr Technology Inc.

Qualcomm Technologies International, Ltd.

Rogerio Guedes Alves

Erik Visser

Lae-Hoon Kim

Kuan-Chieh Yen

Taher Shahbazi Mirzahasanloo

Jacob Jon Bean

Fatemeh Saki

Dongmei Wang

Walter Andres Zuluaga

Kamlesh Lakshminarayanan

Mark Andrew Roberts

Sameer Gadre

Michael Christopher Vartanian

Hannes Pessentheiner

Bryan Neilson

Yi Zhang

Yinyi Guo

Tao Yu

Xuejing Sun

Vahid Montazeri

Walter Zuluaga

Van Nguyen

Walter Andrés Zuluaga

Lester Soon Huat Ngia

Kuan-Chich Yen

Nima Yousefian Jazi

Jeff Chisholm

Amin Fazeldehkordi

A device includes one or more processors configured to obtain data specifying a target signal-to-noise ratio based on a hearing condition of a person and to obtain audio data representing one or more audio signals. The one or more processors are configured to determine, based on the target signal-to-noise ratio, a first gain to apply to first components of the audio data and a second gain to apply to second components of the audio data. The one or more processors are configured to apply the first gain to the first components of the audio data to generate a target signal and to apply the second gain to the second components of the audio data to generate a noise signal. The one or more processors are further configured to combine the target signal and the noise signal to generate an output audio signal.

Audio processing based on target signal-to-noise ratio

Methods, systems, computer-readable media, devices, and apparatuses for synchronized mode transitions are presented. A first device configured to be worn at an ear includes a processor configured to, in a first contextual mode, receive a first mode change request from a second device to transition at a first time to a second contextual mode. The first mode change request is based on detection of a trigger condition. The processor is further configured to, at the first time, transition from the first contextual mode to the second contextual mode based on the first mode change request.

Synchronized mode transition

A first device includes a memory configured to store instructions and one or more processors configured to receive audio signals from multiple microphones. The one or more processors are configured to process the audio signals to generate direction-of-arrival information corresponding to one or more sources of sound represented in one or more of the audio signals. The one or more processors are also configured to and send, to a second device, data based on the direction-of-arrival information and a class or embedding associated with the direction-of-arrival information.

Processing of audio signals from multiple microphones

In general, techniques are described that enable voice activation for computing devices. A computing device configured to support an audible interface that comprises a memory and one or more processors may be configured to perform the techniques. The memory may store a first audio signal representative of an environment external to a user associated with the computing device and a second audio signal sensed by a microphone coupled to a housing of the computing device. The one or more processors may verify, based on the first audio signal and the second audio signal, that the user activated the audible interface of the computing device, and obtain, based on the verification, additional audio signals representative of one or more audible commands.

Voice activation for computing devices

A wearable device may include a processor configured to detect a self-voice signal, based on one or more transducers. The processor may be configured to separate the self-voice signal from a background signal in an external audio signal based on using a multi-microphone speech generative network. The processor may also be configured to apply a first filter to an external audio signal, detected by at least one external microphone on the wearable device, during a listen through operation based on an activation of the audio zoom feature to generate a first listen-through signal that includes the external audio signal. The processor may be configured to produce an output audio signal that is based on at least the first listen-through signal that includes the external signal, and is based on the detected self-voice signal.

Separation of self-voice signal from a background signal using a speech generative network on a wearable device

Methods, systems, and devices for signal processing are described. Generally, as provided for by the described techniques, a wearable device to receive an input audio signal from one or more outer microphones, an input audio signal from one or more inner microphones, and a bone conduction signal from a bone conduction sensor based on the input audio signals. The wearable device may filter the bone conduction signal based on a set of frequencies of the input audio signals, such as a low frequency portion of the input audio signals. For example, the wearable device may apply a filter to the bone conduction signal that accounts for an error in the input audio signals. The wearable device may add a gain to the filtered bone conduction signal and may equalize the filtered bone conduction signal based on the gain. The wearable device may output an audio signal to a speaker.

Active self-voice naturalization using a bone conduction sensor

Methods, systems, computer-readable media, devices, and apparatuses for synchronized mode transitions are presented. A first device configured to be worn at an ear includes a processor configured to, in a first contextual mode, produce an audio signal based on audio data. The processor is also configured to, in the first contextual mode, exchange a time indication of a first time with a second device. The time indication is exchanged based on detection of a first condition of a microphone signal. The processor is further configured to, at the first time, transition from the first contextual mode to a second contextual mode based on the time indication.

Methods, systems, computer-readable media, and apparatuses for audio signal processing are presented. A device for audio signal processing includes a memory configured to store instructions and a processor configured to execute the instructions. When executed, the instructions cause the processor to receive an external microphone signal from a first microphone and produce a hear-through component that is based on the external microphone signal and hearing compensation data. The hearing compensation data is based on an audiogram of a particular user. The instructions, when executed, further cause the processor to cause a loudspeaker to produce an audio output signal based on the hear-through component.

Systems, apparatus, and methods for acoustic transparency

A device includes a memory configured to store instructions and one or more processors configured to execute the instructions. The one or more processors are configured to execute the instructions to receive audio data including a first audio frame corresponding to a first output of a first microphone and a second audio frame corresponding to a second output of a second microphone. The one or more processors are also configured to execute the instructions to provide the audio data to a first noise-suppression network and a second noise-suppression network. The first noise-suppression network is configured to generate a first noise-suppressed audio frame and the second noise-suppression network is configured to generate a second noise-suppressed audio frame. The one or more processors are further configured to execute the instructions to provide the noise-suppressed audio frames to an attention-pooling network. The attention-pooling network is configured to generate an output noise-suppressed audio frame.

Noise suppression using tandem networks

A device includes a memory configured to store instructions and one or more processors configured execute the instructions. The one or more processors are configured execute the instructions to receive audio data including first audio data corresponding to a first output of a first microphone and second audio data corresponding to a second output of a second microphone. The one or more processors are also configured to execute the instructions to provide the audio data to a dynamic classifier. The dynamic classifier is configured to generate a classification output corresponding to the audio data. The one or more processors are further configured to execute the instructions to determine, at least partially based on the classification output, whether the audio data corresponds to user voice activity.

User voice activity detection using dynamic classifier

A wearable device may include a processor configured to perform active noise cancelation (ANC) applied to an input audio signal received by at least one microphone, and detect a self-voice signal, based on one or more transducers. The processor may also be configured to apply a first filter to an external audio signal, detected by at least one external microphone on the wearable device, during a listen through operation based on an activation of the audio zoom feature to generate a first listen-through signal that includes the external audio signal. The processor may also be configured to after the activation of the audio zoom feature terminate a second filter that provides low frequency compensation. The processor may be configured to produce an output audio signal that is based on at least the first listen-through signal that includes the external signal, and is based on the detected self-voice signal.

Seamless listen-through based on audio zoom for a wearable device

Methods, systems, computer-readable media, devices, and apparatuses for synchronized mode transitions are presented. A first device configured to be worn at an ear includes a processor configured to, in a first contextual mode, produce an audio signal based on audio data. The processor is also configured to, in the first contextual mode, exchange a time indication of a first time with a second device. The processor is further configured to, at the first time, transition from the first contextual mode to a second contextual mode based on the time indication.

Methods, systems, and devices for signal processing are described. Generally, as provided for by the described techniques, a wearable device may receive an input audio signal (e.g., including both an external signal and a self-voice signal). The wearable device may detect the self-voice signal in the input audio signal based on a self-voice activity detection (SVAD) procedure, and may implement the described techniques based thereon. The wearable device may perform beamforming operations or other separation procedures to isolate the external signal and the self-voice signal from the input audio signal. The wearable device may apply a first filter to the external signal, and a second filter to the self-voice signal. The wearable device may then mix the filtered signals, and generate an output signal that sounds natural to the user.

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Macomb Township, MI, United States of America

Rogerio Guedes Alves