Methods, apparatus, systems, and articles of manufacture are disclosed to generate spatial audio based on computer vision. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to determine a position of an audio source based on an image generated via a camera, and apply an audio spatialization filter to an audio signal generated by a microphone based on the position of the audio source.

A simultaneous localization and mapping system for a motor vehicle is disclosed and includes a plurality of sensors disposed within a vehicle operable to detect an object proximate the vehicle and generate a plurality of data points representing sensor returns corresponding to the detected objects surrounding the vehicle, and a controller configured to receive the data points representing the sensor returns of the detected objects surrounding the vehicle, to define an occupancy grid based on the data points and to generate vehicle operating instructions based on the defined occupancy grid, wherein the controller is configured to define at least one geometric anchor from the detected data points and localizing the vehicle based on the at least one geometric anchor.

An emergency determining system configured as part of a cellular or mobile network to receive emergency data from public safety answering points and to evaluate the emergency based at least in part on third party data, network data, and the emergency data. For instance, the emergency determining system may determine a level, a geographic area, and/or a response to the emergency and to broadcast, via the cellular or mobile network, notifications to individual's personal electronic devices within the geographic area to alert the individuals to the emergency and to provide instructions related to an appropriate response by the individual.

Disclosed is a technique of determining a measure of proximity between two devices (4, 6). A method implementation of the technique comprises obtaining a first device signature comprising an indication of a first point in time and a first parameter characteristic of a first measurement performed by a first sensor (10) comprised in the first device (4); obtaining a second device signature comprising an indication of a second point in time and a second parameter characteristic of a second measurement performed by a second sensor (12) comprised in the second device (6); and determining, based on the first device signature and the second device signature, the measure of proximity between the first device (4) and the second device (6).

A signal analysis device includes a memory and processing circuitry coupled to the memory and configured to obtain, for a spatial covariance matrix R.sub.j (j is an integral number equal to or larger than 1 and equal to or smaller than J) for modeling spatial characteristics of J (J is an integral number equal to or larger than 2) source signals that are present in a mixed manner, a simultaneous decorrelation matrix P as a matrix in which all P.sup.HR.sub.jP are diagonal matrices, or/and Hermitian transposition P.sup.H thereof, as a parameter for decorrelating components corresponding to the J source signals for observation signal vectors based on observation signals acquired at I (I is an integral number equal to or larger than 2) different positions.

A robotic system is controlled. Audiovisual data representing an environment in which at least part of the robotic system is located is received via at least one camera and at least one microphone. The audiovisual data comprises a visual data component representing a visible part of the environment and an audio data component representing an audible part of the environment. A location of a sound source that emits sound that is represented in the audio data component of the audiovisual data is identified based on the audio data component of the audiovisual data. The sound source is outside the visible part of the environment and is not represented in the visual data component of the audiovisual data. Operation of a controllable element located in the environment is controlled based on the identified location of the sound source.

The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.

Systems, methods, and other embodiments associated with off-duty-cycle-robust machine learning for anomaly detection in assets with random downtimes are described. In one embodiment, a method includes inferring ranges of asset downtime from spikes in a numerical derivative of a time series signal for an asset; extracting an asset downtime signal from the time series signal based on the inferred ranges of asset downtime; determining that the asset downtime signal carries telemetry based on the variance of the asset downtime signal; training a first machine learning model for the asset downtime signal; detecting a first spike in the numerical derivative of the time signal that indicates a transition to asset downtime; and in response to detection of the first spike, monitoring the time series signal for anomalous activity with the trained first machine learning model.

A terminal for outputting multi-channel audio using a plurality of audio devices, the terminal can include a camera; a communication interface configured to communicate with a plurality of first audio devices; and a processor configured to receive device information about the plurality of first audio devices through the communication interface or the camera; configure a multi-channel audio system including at least two second audio devices selected from among the plurality of first audio devices based on the device information; and output audio data through the at least two second audio devices based on audio system information corresponding to the multi-channel audio system.

Tracking and alerting Traffic management system using IoT for smart city to determine a social distance or norms violation between a plurality of communicative pairs, each of the image have plurality of communicative pairs including two communicating entities participating in a corresponding one or more of the communicative acts, the system comprising: CCTV for captured User's data i.e User movements, facial data, Smartphone data in case of accident detection; wireless trans-receiver for event propagation and sending the data to database; Sensor for getting the data of smart phones based on GPS system specially in case of accidental case; processor having CNN technology for analyzing and reverting data to control room based and configured to determine the pairwise social distancing based on particular behavior like movement and stopping or falling; hardware for storing data captured based on classification and analyzed parameters; machine learning for integration of data received from processor or sensors for visualization and processing final data to the citizens or to governments for monitoring and sending data to alarming sensor for non instructive alert if violations of social distancing norms.