Qualcomm Incorporated

Moore Intellectual Property Law, Pllc

Yinyi Guo

Erik Visser

Lae-Hoon Kim

Shuhua Zhang

Fatemeh Saki

Sunkuk Moon

Ravi Choudhary

Prajakt Kulkarni

Jongwon Shin

Minho Jin

Sang-Uk Ryu

Raghuveer Peri

Rogerio Guedes Alves

Pei Xiang

Dongmei Wang

Sanghyun Chi

Andre Gustavo P Schevciw

Yongwoo Cho

Juhan Nam

Wenliang Lu

Hannes Pessentheiner

JunCheol Cho

Mofei Zhu

Eunjeong Koh

Juncheol Cho

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 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 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 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.

A device includes a processor configured to receive audio data samples and provide the audio data samples to a first neural network to generate a first output corresponding to a first set of sound classes. The processor is further configured to provide the audio data samples to a second neural network to generate a second output corresponding to a second set of sound classes. A second count of classes of the second set of sound classes is greater than a first count of classes of the first set of sound classes. The processor is also configured to provide the first output to a neural adapter to generate a third output corresponding to the second set of sound classes. The processor is further configured to provide the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples.

Sound event detection learning

A device includes one or more processors configured to provide audio data samples to a sound event classification model. The one or more processors are also configured to determine, based on an output of the sound event classification model responsive to the audio data samples, whether a sound class of with the audio data samples was recognized by the sound event classification model. The one or more processors are further configured to, based on a determination that the sound class was not recognized, determine whether the sound event classification model corresponds to an audio scene associated with the audio data samples. The one or more processors are also configured to, based on a determination that the sound event classification model corresponds to the audio scene associated with the audio data samples, store model update data based on the audio data samples.

Adaptive sound event classification

A device for multi-modal user input includes a processor configured to process first data received from a first input device. The first data indicates a first input from a user based on a first input mode. The first input corresponds to a command. The processor is configured to send a feedback message to an output device based on processing the first data. The feedback message instructs the user to provide, based on a second input mode that is different from the first input mode, a second input that identifies a command associated with the first input. The processor is configured to receive second data from a second input device, the second data indicating the second input, and to update a mapping to associate the first input to the command identified by the second input.

Multi-modal user interface

Various embodiments provide systems and methods which disclose a command device which can be used to establish a wireless connection, through one or more wireless channels, between the command device and a remote device. An intention code may be generated, prior to, or after, the establishment of the wireless connection, and the remote device may be selected based on the intention code. The command device may initiate a wireless transfer, through one or more wireless channels of the established wireless connection, of an intention code, and receive acknowledgement that the intention code was successfully transferred to the remote device. The command device may then control the remote device, based on the intention code sent to the remote device, through the one or more wireless channels of the established wireless connection between the command device and the remote device.

Wireless control of remote devices through intention codes over a wireless connection

A device to provide information to a visual interface that is mountable to a vehicle dashboard includes a memory configured to store device information indicative of controllable devices of a building and occupant data indicative of one or more occupants of the building. The device includes a processor configured to receive, in real-time, status information associated with the one or more occupants of the building. The status information includes at least one of dynamic location information or dynamic activity information. The processor is configured to generate an output to provide, at the visual interface device, a visual representation of at least a portion of the building and the status information associated with the one or more occupants. The processor is also configured to generate an instruction to adjust an operation of one or more devices of the controllable devices based on user input.

System and method to view occupant status and manage devices of building

An apparatus includes a processor configured to receive one or more media signals associated with a scene. The processor is also configured to identify a spatial location in the scene for each source of the one or more media signals. The processor is further configured to identify audio content for each media signal of the one or more media signals. The processor is also configured to determine one or more candidate spatial locations in the scene based on the identified spatial locations. The processor is further configured to generate audio to playback as virtual sounds that originate from the one or more candidate spatial locations.

Complementary virtual audio generation

A device includes a memory configured to store category labels associated with categories of a natural language processing library. A processor is configured to analyze input audio data to generate a text string and to perform natural language processing on at least the text string to generate an output text string including an action associated with a first device, a speaker, a location, or a combination thereof. The processor is configured to compare the input audio data to audio data of the categories to determine whether the input audio data matches any of the categories and, in response to determining that the input audio data does not match any of the categories: create a new category label, associate the new category label with at least a portion of the output text string, update the categories with the new category label, and generate a notification indicating the new category label.

Audio analytics for natural language processing

An apparatus includes a speech processing engine configured to receive data corresponding to speech and to determine whether a first characteristic associated with the speech differs from a reference characteristic by at least a threshold amount. The apparatus further includes a selection circuit responsive to the speech processing engine. The selection circuit is configured to select a particular speech codebook from among a plurality of speech codebooks based on the first characteristic differing from the reference characteristic by at least the threshold amount. The particular speech codebook is associated with the first characteristic.

Characteristic-based speech codebook selection

A device includes a memory configured to store a user experience evaluation unit. A processor is configured to receive a first user input corresponding to a user command to initiate a particular task, the first user input received via a first sensor. The processor is configured to, after receiving the first user input, receive one or more subsequent user inputs, the one or subsequent user inputs including a second user input received via a second sensor. The processor is configured to initiate a remedial action in response to determining, based on the user experience evaluation unit, that the one or more subsequent user inputs correspond to a negative user experience.

User experience evaluation

In a particular aspect, a speech generator includes a signal input configured to receive a first audio signal. The speech generator also includes at least one speech signal processor configured to generate a second audio signal based on information associated with the first audio signal and based further on automatic speech recognition (ASR) data associated with the first audio signal.

Enhanced speech generation

A method of authenticating a user based on voice recognition of a keyword includes generating, at a processor, clean speech statistics. The clean speech statistics are generated from an audio recording of the keyword spoken by the user during an enrollment phase. The method further includes separating speech data and noise data from noisy input speech using the clean speech statistics during an authentication phase. The method also includes authenticating the user by comparing the speech data to the clean speech statistics or by comparing the noisy input speech to noisy speech statistics. The noisy speech statistics are based at least in part on the noise data.

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San Diego, CA, United States of America

Yinyi Guo