A first intelligent assistant computing device configured to receive and respond to natural language inputs provided by human users syncs to a reference clock of a wireless computer network. The first intelligent assistant computing device receives a communication sent by a second intelligent assistant computing device indicating a signal emission time at which the second intelligent assistant computing device emitted a position calibration signal. The first intelligent assistant computing device records a signal detection time at which the position calibration signal was detected. Based on a difference between 1) the signal emission time and the signal detection time, and 2) a known propagation speed of the position calibration signal, a distance between the first and second intelligent assistant computing devices is calculated.

A method for determining the location of a frequency receiver device with respect to at least two frequency originator devices, each of a current location, the method including synchronizing a clock of the frequency receiver device with a clock of one of the at least two frequency originator devices; receiving by the frequency receiver device, a message including an identification code configured for identifying one of the at least two frequency originator devices and obtaining a broadcast time and a current location of the one of the at least two frequency originator devices by looking up a table correlating the at least two frequency originator devices and their respective broadcast times and current locations; calculating a time of flight of the message by calculating the difference between a receive time at which the message is received by the frequency receiver device and the broadcast time.

Disclosed is a system and method for generating a model of the geometric relationships between various audio sources recorded by a multi-camera system. The spatial audio scene module associates source signals, extracted from recorded audio, of audio sources to visual objects identified in videos recorded by one or more cameras. This association may be based on estimated positions of the audio sources based on relative signal gains and delays of the source signal received at each microphone. The estimated positions of audio sources are tracked indirectly by tracking the associated visual objects with computer vision. A virtual microphone module may receive a position for a virtual microphone and synthesize a signal corresponding to the virtual microphone position based on the estimated positions of the audio sources.

Aspects of the present disclosure provide a first apparatus comprising a transmitter, a receiver, a processor, and a UI. The transmitter is configured to command a second apparatus to emit at least one tone. The receiver is configured to receive the at least one tone. The processor is configured to determine at least one of: a signal strength and a direction associated with the at least one tone. The UI is configured to output an indication of a current location of the second apparatus based, at least in part, on the determined signal strength or the determined direction. According to an example, at least one of the request or the at least one tone may be a high-frequency or ultrasonic signal.

A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.

Orientation-independent antennas and associated beamforming circuits, to provide polarization-independent determination of position. An Indoor Positioning System (IPS) may utilize beacon or tag devices equipped with orientation-independent antennas to determine the location of nearby objects. The system can exist in many different customizable configurations, sometimes utilizing orientation-independent antennas embedded in smartphones that serve as beacon or tag devices. The devices, systems and methods described herein may be used for an IPS in a residential setting, a commercial setting (like a department store), an event or workplace, or an industrial setting.

A positioning system and method thereof are provided in this disclosure. The positioning method includes steps of: emitting a radiation from a first electronic apparatus to a second electronic apparatus and starting to accumulate a time count; sensing the radiation on the second electronic apparatus and sending a first ultrasonic signal from the second electronic apparatus to the first electronic apparatus; sensing the first ultrasonic signal by a plurality of ultrasound sensors on the first electronic apparatus and calculating a plurality of first time periods started from the radiation is emitted until the first ultrasonic signal is sensed by the ultrasound sensors; calculating a plurality of first relative distances between the ultrasound sensors and a first ultrasound emitter on the second electronic apparatus; and locating a first relative position of the second electronic apparatus relative to the first electronic apparatus according to the first relative distances.

A positioning system and method thereof are provided in this disclosure. The positioning method includes steps of: emitting a radiation from a first electronic apparatus to a second electronic apparatus and starting to accumulate a time count; sensing the radiation on the second electronic apparatus and sending a first ultrasonic signal from the second electronic apparatus to the first electronic apparatus; sensing the first ultrasonic signal by a plurality of ultrasound sensors on the first electronic apparatus and calculating a plurality of first time periods started from the radiation is emitted until the first ultrasonic signal is sensed by the ultrasound sensors; calculating a plurality of first relative distances between the ultrasound sensors and a first ultrasound emitter on the second electronic apparatus; and locating a first relative position of the second electronic apparatus relative to the first electronic apparatus according to the first relative distances.

A neural network model, such as a deep neural network (DNN), is trained using many speech examples to perform beam selection in a microphone array-based speech processing system. The DNN is trained using many different speech examples that are labeled with position or direction information relative to a training microphone array. The DNN may then be trained to recognize a direction of incoming speech so that at runtime the trained DNN may process input audio data from a microphone array and may output to a beam selector an indicator of the desired beam that may be selected for further processing. The DNN may be configured to output a beam index and/or coordinates (or other position data) corresponding to an estimated location of the detected speech. The DNN may also be configured to output acoustic unit data corresponding to speech units (for example corresponding to phonemes, senons, etc. such as those of a detected wakeword or other word).

A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.