Abstract: Smart home functionality awareness may improve the ability for a smart home system to identify existing and absent smart home skills or smart home devices that may be used to perform a specific task requested by a user. This smart home functionality awareness improves the ability of a smart home system to identify an existing capability of the smart home system that addresses requested electronic device command. This smart home functionality awareness improves the ability of a smart home system to identify a recommended capability, where the recommended capability provides improved functionality over the existing capability for addressing the requested electronic device command. This recommended capability may be identified based on determining that existing capabilities are sufficient to accomplish a task with low performance, and further identify what additional resources may be needed to improve the ability of the smart home system to execute the requested electronic device command.

Abstract: System and techniques to map a workspace for a robotic device are described herein. The robotic device has a movable portion with a sensor that is used to capture readings from part of the workspace. These readings are used to map a portion of the workspace and then to plot a new position of the movable portion to get readings for another part of the workspace. As the technique iterates, the robotic device is able to safely and efficiently map the entirety of the workspace.

Abstract: An audio processing device and method uses audio signals from a virtual rotating microphone for acoustic angle of arrival detection using a doppler effect technique.

Abstract: Techniques are provided herein for identifying the user of audio earbuds. In particular, a wearer's head filters an audio signal, and the audio filtering capabilities of a user's head are used as a biometric feature. One earbud can be used as an audio emitter and the other earbud as an audio receiver. A broadband sound can be generated by the speaker in one earbud and received at the microphone of the other earbud. The received sound is filtered by the user's head and the head characterization of the received filtered sound can be used to identify the user. In particular, the material properties of the user's head change the signal, such that the received signal at the microphone of the other earbud is different from the transmitted signal. The differences are unique to the user's head due to physiological variances among people, and can be used to identify the user.

Abstract: An apparatus, including: an interface configured to receive a target end-effector pose of a cobot; processing circuitry configured to: generate in a generic robot model a joint trajectory based on the target end-effector pose; employ a trained neural network model to map the joint trajectory generated in the generic robot model into a cobot model; and generate a movement instruction to control a movement of the cobot based on the joint trajectory mapped to the cobot model, wherein the generic robot model has a number of degrees of freedom that is equal to or greater than that of the cobot model.

Abstract: A system that includes at least one memory and at least one processor, communicatively coupled to the at least one memory. In some examples, the at least one processor is to execute instructions stored on the at least one memory that cause the at least one processor to: determine a location of at least one display based on radiation and/or emitted image.

Abstract: A self-reconfigurable controller for a robot, including: input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot; and processing circuitry that is operable to: register the I/O peripheral devices and associated functionalities; receive a command for the robot to perform a task; conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices; and generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task.

Abstract: Methods, apparatus, systems, and articles of manufacture for real-time voice type detection in audio data are disclosed. An example non-transitory computer-readable medium disclosed herein includes instructions, which when executed, cause one or more processors to at least identify a first vocal effort of a first audio segment of first audio data and a second vocal effort of a second audio segment of the first audio data, train a neural network including training data, the training data including the first vocal effort, the first audio segment, the second audio segment, and the second vocal effort, and deploy the neural network, the neural network to distinguish between the first vocal effort and the second vocal effort.

Abstract: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to enhance and audio signal.

Abstract: Methods, apparatus, systems, and articles of manufacture to detect the location of sound sources external to computing devices are disclosed. An apparatus, to determine a direction of a source of a sound relative to a computing device, includes a cross-correlation analyzer to generate a vector of values corresponding to a cross-correlation of first and second audio signals corresponding to the sound. The first audio signal is received from a first microphone of the computing device. The second audio signal is received from a second microphone of the computing device. The apparatus also includes a location analyzer to use a machine learning model and a set of the values of the vector to determine the direction of the source of the sound.

Abstract: Systems, apparatuses and methods may provide for technology that adjusts a plurality of weights in a neural network model and adjusts a plurality of activation functions in the neural network model. The technology may also output the neural network model in response to one or more conditions being satisfied by the plurality of weights and the plurality of activation functions. In one example, two or more of the activation functions are different from one another and the activation functions are adjusted on a per neuron basis.

Abstract: Hearing protection and communication apparatus using vibration sensors are disclosed. An example wearable electronic device includes means for transducing vibrations associated with speech into a first signal; means for transducing sound associated with ambient noise into a second signal; and means for processing to cause a speaker to output a signal to reduce the ambient noise; detect an identifier in the speech; and cause audio data representative of the speech to be transmitted to a second device associated with the identifier.

Abstract: A system is described herein. The system includes at least one hardware processor that is configured to identify a pre-determined acoustic barrier filter, wherein the acoustic barrier filter coincides with the physical acoustic barrier and receive an audio signal within a time window at the first microphone and the second microphone. The hardware processor is also configured to calculate a first measure of variability, a second measure of variability, a third measure of variability, and a fourth measure of variability. The hardware processor further concatenates the first measure of variability, the second measure of variability, the third measure of variability, and the fourth measure of variability to form a feature vector, and inputs the feature vector into a location classifier to obtain an audio source location.

Abstract: A component of an Autonomous Vehicle (AV) system, the component having at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to decode data encoded in a signal, wherein the data identifies a pattern of protrusions embedded in a driving surface, the signal being received from at least one vehicle sensor resulting from a vehicle driving over the pattern of protrusions in the driving surface.

Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: This disclosure describes systems, methods, and devices related to reducing noise in and improving speech signals and radio signals. A device may identify a radio frequency signal received by radio frequency circuity at a time; apply a Short Time Fourier Transform (STFT) to the radio frequency signal to generate a STFT signal; identify a signal-to-noise ratio associated with the radio frequency signal at the time; identify a packet-error-rate associated with the radio frequency signal at the time; identify activity when the multiplexed radio frequency signal is received by the radio frequency circuity at the time; select, based on the signal-to-noise ratio, the packet-error-rate, and the activity, a spectral mask to be applied to the STFT signal; apply the selected spectral mask to the STFT signal to generate a clean STFT signal; and apply an inverse STFT to the clean STFT signal to generate a clean radio frequency signal.

Abstract: Techniques are disclosed to use existing vehicle speakers alone or in conjunction with other sensors (e.g. SRS sensors and/or microphones) that may already be implemented as part of the vehicle to identify acoustic signatures. Suitable low-cost and widely available hardware components (e.g., relays) may be used to modify the vehicle's existing speakers for a bi-directional mode of operation. Moreover, the vehicle's existing of audio amplifiers may be used to amplify signals collected by the speakers when operating in “reverse,” and process these collected signals to determine vehicle state information.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory; instructions; and processor circuitry to execute the instructions. The processor circuitry executes the instructions to extract static and dynamic data from a packet associated with a request for service by an edge device, the static data to change less frequently than the dynamic data. The processor circuitry executes the instructions to generate a first plurality of probability distributions using the static data. The processor circuitry executes the instructions to generate a second plurality of probability distributions using the dynamic data. The processor circuitry executes the instructions to calculate a confidence value for a first helper compute unit of a plurality of helper compute units, the confidence value. The processor circuitry executes the instructions to assign the first helper compute unit the request for service based on the confidence value.

Abstract: A system, article, and method of acoustic angle of arrival detection uses both same-time and delayed-time audio signal value comparisons in a time domain that are input to a classifier neural network.