Qualcomm Incorporated

Fatemeh Saki

Erik Visser

Lae-Hoon Kim

Dongmei Wang

Rogerio Guedes Alves

Yinyi Guo

Taher Shahbazi Mirzahasanloo

Ravi Choudhary

Sunkuk Moon

Shuhua Zhang

Anne Katrin Konertz

Cheng-Yu Hung

Sharon Kaziunas

Hannes Pessentheiner

Eunjeong Koh

Yoon Mo Yang

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

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

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 execute instructions to determine a first phase based on a first audio signal of first audio signals and to determine a second phase based on a second audio signal of second audio signals. The one or more processors are also configured to execute the instructions to apply spatial filtering to selected audio signals of the first audio signals and the second audio signals to generate an enhanced audio signal. The one or more processors are further configured to execute the instructions to generate a first output signal including combining a magnitude of the enhanced audio signal with the first phase and to generate a second output signal including combining the magnitude of the enhanced audio signal with the second phase. The first output signal and the second output signal correspond to an audio zoomed signal.

Audio zoom

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

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.

Seamless listen-through for a wearable device

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

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

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

Methods, systems, computer-readable media, and apparatuses for signal enhancement are presented. One example of such an apparatus includes a receiver configured to produce a remote speech signal from information carried by a wireless signal; a signal canceller configured to perform a signal cancellation operation on a local speech signal to generate a room response; and a filter configured to filter the remote speech signal according to the room response to produce a filtered speech signal. In this example, the signal cancellation operation is based on the remote speech signal as a reference signal.

Acoustic path modeling for signal enhancement

Methods, systems, and devices for signal processing are described. Generally, in one example as provided for by the described techniques, a wearable device includes a processor configured to retrieve a plurality of external microphone signals that includes audio sound from outside of the device from a memory; to separate, based on at least information from an internal microphone signal, a self-voice component from a background component; to perform a first listen-through operation on the separated self-voice component to produce a first listen-through signal; and to produce an output audio signal that is based on at least the first listen-through signal, wherein the output audio signal includes an audio zoom signal that includes audio sound of the plurality of external microphone signals.

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.

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San Diego, CA, United States of America

Fatemeh Saki