Qualcomm Incorporated

Softmax, Inc.

Other

University of California

Moore Intellectual Property Law, Pllc

Erik Visser

Lae-Hoon Kim

Yinyi Guo

Shuhua Zhang

Fatemeh Saki

Rogerio Guedes Alves

Sunkuk Moon

Pei Xiang

Jeremy Patrick Toman

Dongmei Wang

Asif Mohammad

Jongwon Shin

Raghuveer Peri

Ian Ernan Liu

Taher Shahbazi Mirzahasanloo

Phuong Lam Ton

Kwokleung Chan

S M Akramus Salehin

Prajakt Kulkarni

Vahid Montazeri

Ravi Choudhary

Samir Kumar Gupta

Shankar Thagadur Shivappa

Hyun Jin Park

Brian Lee Momeyer

Andre Gustavo P Schevciw

Jacob Jon Bean

Juhan Nam

Te-Won Lee

Dinesh Ramakrishnan

Sanghyun Chi

Shaun William Van Dyken

Jeffrey Clinton Shaw

Hannes Pessentheiner

Troy Schultz

Sreekanth Narayanaswamy

Cheng-Yu Hung

Babak Forutanpour

Francis B MacDougall

Minho Jin

Sang-Uk Ryu

Joseph Robert Fitzgerald

Eric Stephen Mattis

Anne Katrin Konertz

Michael Joseph Contour

Jimeng Zheng

Kwok-Leung Chan

Xiaoxin Zhang

Ren Li

Siddhartha Goutham Swaminathan

Hasib Siddiqui

Wade Lyle Heimbigner

Isaac Garcia Munoz

Anthony P Mauro

Ye Jiang

Ricardo De Jesus Bernal Castillo

Yongwoo Cho

Vasudev Nayak

Asif Iqbal Mohammad

Kyungguen Byun

Sharon Kaziunas

Ernan Liu

Andre Gustavo Pucci Schevciw

Wenliang Lu

Haiyin Wang

Van Nguyen

Tom Davis

Soo Jin Park

Eunjeong Koh

Yoon Mo Yang

Arvind Krishna Sridhar

Rishabh Tyagi

Bicheng Jiang

JunCheol Cho

Joseph Jyh-huei Huang

Hung Chun Lin

Mofei Zhu

Andre Gustavo Schevciw

Juncheol Cho

Phuong Ton

Asif I Mohammad

Luis D Oliveira

A device to perform target sound detection includes a memory including a buffer configured to store audio data. The device includes one or more processors coupled to the memory. The one or more processors are configured to receive the audio data from the buffer. The one or more processors are configured to detect the presence or absence of one or more target non-speech sounds in the audio data. The one or more processors are further configured to generate a user interface signal, to indicate one of the one or more target non-speech sounds has been detected, and provide the user interface signal to an output device. The device further comprises the output device that is configured to output a visual representation associated with the one of the one or more target non-speech sounds has been detected.

Method and apparatus for target sound detection

A device to perform speech enhancement includes one or more processors configured to process image data to detect at least one of an emotion, a speaker characteristic, or a noise type. The one or more processors are also configured to generate context data based at least in part on the at least one of the emotion, the speaker characteristic, or the noise type. The one or more processors are further configured to obtain input spectral data based on an input signal. The input signal represents sound that includes speech. The one or more processors are also configured to process, using a multi-encoder transformer, the input spectral data and the context data to generate output spectral data that represents a speech enhanced version of the input signal.

Context-data based speech enhancement

A vehicle includes an interface device, an in-vehicle control unit, a functional unit, and a processing circuitry. The interface device receives a spoken command to identify an in-cabin vehicle zone of two or more in-cabin vehicle zones of the vehicle, and receives background audio data concurrently with a portion of the spoken command. The in-cabin vehicle control unit separates the background audio data from the spoken command, and selects which in-cabin vehicle zone of the two or more in-cabin vehicle zones is identified by the spoken command. The functional unit controls a function within the vehicle. The processing circuitry stores, to a command buffer, data processed from the received spoken command, and controls, based on the data processed from the received spoken command, the functional unit using audio input received from the selected in-cabin vehicle zone.

In-vehicle voice command control

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

Gesture-responsive modification of a generated sound field is described.

Systems, methods, apparatus, and computer-readable media for gestural manipulation of a sound field

A device includes one or more processors configured to receive sensor data from one or more sensor devices. The one or more processors are also configured to determine a context of the device based on the sensor data. The one or more processors are further configured to select a model based on the context. The one or more processors are also configured to process an input signal using the model to generate a context-specific output.

Context-based model selection

A device to process speech includes a speech processing network that includes an input configured to receive audio data. The speech processing network also includes one or more network layers configured to process the audio data to generate a network output. The speech processing network includes an output configured to be coupled to multiple speech application modules to enable the network output to be provided as a common input to each of the multiple speech application modules.

Shared speech processing network for multiple speech applications

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

A device includes a memory configured to store untransformed ambisonic coefficients at different time segments. The device includes one or more processors configured to obtain the untransformed ambisonic coefficients at the different time segments, where the untransformed ambisonic coefficients at the different time segments represent a soundfield at the different time segments. The one or more processors are configured to apply one adaptive network, based on a constraint that includes preservation of a spatial direction of one or more audio sources in the soundfield at the different time segments, to the untransformed ambisonic coefficients at the different time segments to generate transformed ambisonic coefficients at the different time segments, wherein the transformed ambisonic coefficients at the different time segments represent a modified soundfield at the different time segments, that was modified based on the constraint. The one or more processors are also configured to apply an additional adaptive network.

Transform ambisonic coefficients using an adaptive network for preserving spatial direction

A device includes a memory configured to store instructions. The device also includes one or more processors configured to execute the instructions to provide context and one or more items of interest corresponding to the context to a dependency network encoder to generate a semantic-based representation of the context. The one or more processors are also configured to provide the context to a data dependent encoder to generate a context-based representation. The one or more processors are further configured to combine the semantic-based representation and the context-based representation to generate a semantically-augmented representation of the context.

Semantically-augmented context representation generation

A device includes one or more processors configured to receive an input audio signal. The one or more processors are also configured to process the input audio signal based on a combined representation of multiple sound sources to generate an output audio signal. The combined representation is used to selectively retain or remove sounds of the multiple sound sources from the input audio signal. The one or more processors are further configured to provide the output audio signal to a second device.

Audio processing using sound source representations

A device to perform target sound detection includes one or more processors. The one or more processors include a buffer configured to store audio data and a target sound detector. The target sound detector includes a first stage and a second stage. The first stage includes a binary target sound classifier configured to process the audio data. The first stage is configured to activate the second stage in response to detection of a target sound. The second stage is configured to receive the audio data from the buffer in response to the detection of the target sound.

Growing community of inventors

San Diego, CA, United States of America

Erik Visser