Qualcomm Incorporated

Moore Intellectual Property Law, Pllc

Lae-Hoon Kim

Erik Visser

Shuhua Zhang

Yinyi Guo

Rogerio Guedes Alves

Fatemeh Saki

Dongmei Wang

Sunkuk Moon

Pei Xiang

Raghuveer Peri

Asif Mohammad

Taher Shahbazi Mirzahasanloo

Jongwon Shin

Phuong Lam Ton

Jeremy Patrick Toman

S M Akramus Salehin

Vahid Montazeri

Shankar Thagadur Shivappa

Ian Ernan Liu

Jacob Jon Bean

Juhan Nam

Ravi Choudhary

Samir Kumar Gupta

Prajakt Kulkarni

Sang-Uk Ryu

Sanghyun Chi

Shaun William Van Dyken

Jeffrey Clinton Shaw

Troy Schultz

Cheng-Yu Hung

Hannes Pessentheiner

Francis B MacDougall

Dinesh Ramakrishnan

Minho Jin

Anne Katrin Konertz

Jimeng Zheng

Sharon Kaziunas

Kyungguen Byun

Babak Forutanpour

Xiaoxin Zhang

Hyun Jin Park

Hasib Siddiqui

Isaac Garcia Munoz

Ye Jiang

Ricardo De Jesus Bernal Castillo

Andre Schevciw

Andre Gustavo P Schevciw

Yongwoo Cho

Vasudev Nayak

Sreekanth Narayanaswamy

Wenliang Lu

Haiyin Wang

Van Nguyen

JunCheol Cho

Mofei Zhu

Soo Jin Park

Yoon Mo Yang

Arvind Krishna Sridhar

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 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

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 determine, based on data descriptive of two or more audio environments, a geometry of a mutual audio environment. The one or more processors are also configured to process audio data, based on the geometry of the mutual audio environment, for output at an audio device disposed in a first audio environment of the two or more audio environments.

Augmented audio for communications

A device includes one or more processors configured to perform signal processing including a linear transformation and a non-linear transformation of an input signal to generate a reference target signal. The reference target signal has a linear component associated with the linear transformation and a non-linear component associated with the non-linear transformation. The one or more processors are also configured to perform linear filtering of the input signal by controlling adaptation of the linear filtering to generate an output signal that substantially matches the linear component of the reference target signal.

Linearization of non-linearly transformed signals

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

A device includes one or more processors configured to obtain audio signals representing sound captured by at least three microphones and determine spatial audio data based on the audio signals. The one or more processors are further configured to determine a metric indicative of wind noise in the audio signals. The metric is based on a comparison of a first value and a second value. The first value corresponds to an aggregate signal based on the spatial audio data, and the second value corresponds to a differential signal based on the spatial audio data.

Spatial audio wind noise detection

A device to perform speech enhancement includes one or more processors 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 context data to generate output spectral data that represents a speech enhanced version of the input signal.

Context-based speech enhancement

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San Diego, CA, United States of America

Lae-Hoon Kim