Acoustic signals from an acoustic event are captured via sensing nodes of sensor group(s) that comprise a group of sensing nodes at a location comprising spatial boundaries. Each of the sensing nodes comprise a sensor area. Each of the sensor group(s) is based on: range limits of each of the sensing nodes; shared sensing areas of the sensing nodes; and intersections between the sensor area for each of the sensing nodes and the spatial boundaries. Solutions(s) are generated by processing the acoustic signals. The solution(s) indicate the location of the acoustic event. A strength of solution compliance value for at least one of the solution(s) is determined. A refined solution is generated employing: sensor contributions of sensing nodes; and the strength of solution compliance value with the spatial boundaries and at least one of the solution(s). A report is created comprising the location of the acoustic event.

Methods are disclosed that, in some aspects, provide for the determination of alarm events or non-alarm events based on data received from various sensors monitoring one or more entry points of a premises. Non-alarm events may, for example, include a seismic event or a knock event. Determining whether the data received from the various sensors is an alarm or non-alarm event may be based on data received from two or more sensors monitoring two or more entry points of the premises. Further, data related to the non-alarm event that occurred at the premise may be compared to data related to non-alarm events that occurred at other premises and, based on the comparison, one or more authorities may be alerted to the non-alarm event.

Tampering with an audio/video (A/V) recording and communication device is detected based on audio data captured by a microphone and/or video data captured by a camera of the A/V recording and communication device. The detection of the tampering may be based on, for example, processing of the audio and/or video data. Additional data may be collected and/or other actions taken in response to detection of the tampering.

In order to facilitate fixed-point monitoring over a wide area and for a long time, this water area monitoring device comprises: a detection unit for information, such as water area intrusion, which detects, from sound data that is acquired by means of optical fibers installed in the water or the water bottom and is data pertaining to a sound or a vibration at the respective positions of the optical fibers, at least any one among sounds of water area intrusion or the like that indicate intrusion into a target water area or water area intrusion or the like, which is a prescribed behavior in the target water area, at a time at which sound data is acquired, and changes in sounds or vibrations not caused by the water area intrusion or the like; and an output unit which outputs information indicating the sound such as water area intrusion.

Various aspects of the disclosure relate to monitoring a physical location to determine and/or predict anomalous activities. One or more machine learning algorithms may be used to analyze inputs from one or more sensors, cameras, audio recording equipment, and/or any other types of sensors to detect anomalous measurements/patterns. Notifications may be sent one or more devices in a network based on the detection.

Systems and methods of non-line-of-sight passive detection and integrated early warning of an unmanned aerial system by a plurality of acoustic sensors are described. In some embodiments, the plurality of acoustic sensors is positioned within an intra-netted array in depth according to at least one of a terrain, terrain features, or man-made objects or structures. The acoustic sensors are capable of detecting and tracking unmanned aerial systems in non-line-of-sight environments. In some embodiments, the acoustic sensors may be in communication with internal electro-optical components or other external sensors, with orthogonal signal data then transmitted to remote observation stations for correlation, threat determination and if required, mitigation. The unmanned aerial systems may be classified by type and a threat level associated with the unmanned aerial system may be determined.

Event-related data based on an image that has been captured is generated. Coverage information relating to imaging range is compared to the event-related data. The cameras that can capture an image of the event, based on a comparing result, are listed so that an operator can select one of the listed cameras.

Disclosed is a monitoring system, having: a keybox configured to store a key; a monitoring device, wherein via a monitoring device controller, the monitoring device is configured to: detect a first alert condition indicative of the keybox being outside a communication range of shortwave radio; and transmit a first alert to a remote implement that is indicative of an occurrence of the first alert condition.

A method and system for detecting and localizing a target audio event in an audio clip is disclosed. The method and system use utilizes a hierarchical approach in which a dilated convolutional neural network to detect the presence of the target audio event anywhere in an audio clip based on high level audio features. If the target audio event is detected somewhere in the audio clip, the method and system further utilizes a robust audio vector representation that encodes the inherent state of the audio as well as a learned relationship between state of the audio and the particular target audio event that was detected in the audio clip. A bi-directional long short term memory classifier is used to model long term dependencies and determine the boundaries in time of the target audio event within the audio clip based on the audio vector representations.

A smart sensor early warning notification system. Certain aspects of the present disclosure provide for a smart sensor early warning notification system comprising a smart sensor coupled to a door and configured to detect one or more unauthorized access events, including sawing, blunt force and other atypical vibrations to the door. A smart sensor may be communicably engaged with a local alarm to communicate sensor data received at the smart sensor. The local alarm may be configured to process the sensor data received from the smart sensor and trigger an alarm event and/or notification or pre-event detection alert in response to the sensor data. A door controller may be configured to send different pulses or messages to indicate the type of alarm event to an alarm management server and/or access control servers via public/private cloud.