Abstract: Methods, apparatus, and software for implementing dual Bayesian encoding-decoding for text-to-code transformations. In one aspect, a multi-model probabilistic source code model employing dual Bayesian encoder-decoder models is used to convert natural language (NL) inputs (aka requests) into source code. An NL input is processed to generate a Probabilistic Distribution (PD) of Source code (SC) tokens in an SC token sequence and a PD of Abstract Syntax Tree (AST) tokens in an AST token sequence, wherein each SC token is associated with a respective AST token, and each of the SC and AST tokens have a respective PD. One or more fixing rules are applied to one or more tokens SC tokens that are identified as needing fixing, wherein the fixing rule are selected in consideration of the PDs of the SC tokens and the PDs of their associated AST tokens.

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: 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: 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 detection of acoustic source aiming direction uses multiple microphones.

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

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: Methods, apparatus, and software for implementing dual Bayesian encoding-decoding for text-to-code transformations. In one aspect, a multi-model probabilistic source code model employing dual Bayesian encoder-decoder models is used to convert natural language (NL) inputs (aka requests) into source code. An NL input is processed to generate a Probabilistic Distribution (PD) of Source code (SC) tokens in an SC token sequence and a PD of Abstract Syntax Tree (AST) tokens in an AST token sequence, wherein each SC token is associated with a respective AST token, and each of the SC and AST tokens have a respective PD. One or more fixing rules are applied to one or more tokens SC tokens that are identified as needing fixing, wherein the fixing rule are selected in consideration of the PDs of the SC tokens and the PDs of their associated AST tokens.

Abstract: An example apparatus including processor circuitry to execute instructions to join a video conferencing meeting including a first audio channel, the first audio channel including first binaural audio at a first angle; in response to a user joining a second audio channel in the video conferencing meeting, select a second angle for the second audio channel and a third angle for the first audio channel; generate a superimposed binaural audio including second binaural audio and third binaural audio; and output the superimposed binaural audio such that first audio data from the first audio channel included in the third binaural audio is to appear to originate from a first position based on the third angle and second audio data from the second audio channel included in the second binaural audio is to appear to originate from a second position based on the second angle.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to identify and interpret code. An example apparatus includes a natural language (NL) processor to process NL features to identify a keyword, an entity, and an intent of an NL string included in an input retrieved from a user; a database driver to transmit a query to a database including an ontological representation of a version control system, wherein the query is a parameterized semantic query including the keyword, the entity, and the intent of the NL string; and an application programming interface (API) to present to the user a code snippet determined based on the query, the code snippet being at least one of uncommented or non-self-documented.

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: Technologies for using a shifted neural network include a compute device to determine a shift-based activation function of the shifted neural network. The shift-based activation function is a piecewise linear approximation of a transcendental activation function and is defined by a plurality of line segments such that a corresponding slope of each line segment is a power of two. The compute device further trains the shifted neural network based on shift-based input weights and the determined shift-based activation function.

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: 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: An example apparatus for detecting speech includes an image receiver to receive depth information corresponding to a face. The apparatus also includes a landmark detector to detect the face comprising lips and track a plurality of descriptor points comprising lip descriptor points located around the lips. The apparatus further includes a descriptor computer to calculate a plurality of descriptor features based on the tracked descriptor points. The apparatus includes a pattern generator to generate a visual pattern of the descriptor features over time. The apparatus also further includes a speech recognition engine to detect speech based on the generated visual pattern.

Abstract: A wearable gesture recognition device is disclosed that provides gesture recognition for gestures that may include a hold or steady-state component, and may account and adapt for real-time fit-level changes. The wearable gesture recognition device may integrate a photoplethysmographic (PPMG) and a piezoelectric (PZE) sensor such that respective sensor signals may be used individually, or in concert for gesture recognition. Thus the wearable gesture recognition device generally disclosed herein may advantageously perform gesture recognition through the fusion of PPMG and PZE signals. To support continuous gesture recognition, the wearable gesture recognition device may use a low-power activity detection scheme that analyzes a PZE signal prior to higher-power gesture classification. Moreover, the wearable gesture recognition device may provide power management by controlling a duty-cycle of the PPMG sensor without necessarily reducing recognition performance.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for detection of a change of electromagnetic field in response to a gesture, to identify the gesture that caused the field change. In one instance, the apparatus may include a first conducting component having first features for the disposal on or around a portion of a user's body, to generate an electromagnetic field in response to a receipt of a source signal. The apparatus may further include a second conducting component having second features for the disposal on or around a portion of the user's body, at a distance from the first conducting component, to provide an indication of a change in the electromagnetic field over time, to identify a change of a position of the user's body portion (gesture) that causes the change in the electromagnetic field. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an apparatus for a user's physiological context measurements. In one instance, the apparatus may include a processing block and first and second piezoelectric sensors coupled with the processing block. The first and second sensors may include respectively first and second electrodes to provide contact with a user's body in response to mounting of the apparatus on the user's body. The processing block may comprise a multi-modal sensing system configured to perform measurements of a user's physiological context during the contact of the user's body with the first and second electrodes, based at least in part on a voltage signal generated by the user's body and provided to the processing block via the first and second electrodes. Other embodiments may be described and/or claimed.