A smart home monitoring apparatus which drives the smart home monitoring apparatus by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for Internet of Things. The smart home monitoring apparatus and method according to the exemplary embodiment of the present disclosure includes generating a spatial map of a monitoring area, transmitting a first inaudible sound wave signal to the monitoring area to receive a first inaudible sound wave echo signal, predicting a possibility of abnormal state occurrence of the monitoring area through the first inaudible sound wave echo signal based on the spatial map of the monitoring area, obtaining an image of the monitoring area photographed by the camera when the abnormal state occurrence of the monitoring area is predicted, and determining whether an abnormal state occurs in the monitoring area by analyzing the obtained image.

In accordance with an embodiment, a system for surveillance includes an audio signal analyzer, wherein the audio signal analyzer is configured to receive one or more audio microphone signals, where the audio signal analyzer is configured to determine a pattern matching result by determining whether the one or more microphone signals comprise at least one audio pattern of one or more predefined audio patterns; an air pressure change determiner, where the air pressure change determiner is configured to receive an air pressure change signal indicating an air pressure change; and an evaluator, wherein the evaluator is configured to indicate, depending on the pattern matching result and depending on the air pressure change, that a predefined event occurred.

In accordance with an embodiment, a system includes an audio signal analyzer configured to receive one or more audio microphone signals, and configured to determine a first pattern matching result by determining whether the one or more microphone signals include at least one audio pattern of one or more predefined audio patterns. Moreover, the system includes an air pressure signal analyzer configured to receive an air pressure change signal indicating an air pressure change, or configured to receive an air pressure signal indicating a current air pressure. The air pressure signal analyzer is configured to determine a second pattern matching result by determining whether the air pressure signal or the air pressure change signal includes at least one pressure sensor pattern. Furthermore, the system comprises an evaluator configured to indicate, depending on the first pattern matching result and depending second pattern matching result that a predefined event occurred.

The methods and systems of the present disclosure can monitor, by a microprocessor of a first device, changes in pressure over time at the first device; detect, by the microprocessor, a first measurement in the pressure over time; and provide, by the microprocessor, a first alert based on the detection of the first measurement.

An acoustic array system for anomaly detection is provided. The acoustic array system (100) performs a scan (or a progressive scan of frequencies) of a given volume by transmitting one or more signals, and receives one or more reflected signals from objects within the volume. The reflected signals are then amplified and converted to a set of digital signals. Features of the set of digital signals are extracted both in time and frequency domains. The acoustic array system (100) further performs a comparison of these set of digital extracted features with the reflected signals via machine learning techniques. Based on the comparison, the acoustic array system detects one or more anomalies.

An acoustic array system for anomaly detection is provided. The acoustic array system (100) performs a scan (or a progressive scan of frequencies) of a given volume by transmitting one or more signals, and receives one or more reflected signals from objects within the volume. The reflected signals are then amplified and converted to a set of digital signals. Features of the set of digital signals are extracted both in time and frequency domains. The acoustic array system (100) further performs a comparison of these set of digital extracted features with the reflected signals via machine learning techniques. Based on the comparison, the acoustic array system detects one or more anomalies.

A multi-spectral detection device is provided that includes a housing, a bezel spacer, a microphone, an end cap and electro-optical sensors mounted to the end cap. The housing has an inner chamber that holds sensor components. The bezel spacer has a first end and an opposed, second end. The first end of the bezel spacer extends from a first end of the housing. The bezel spacer has a central bezel passage leading to the inner chamber of the housing. The bezel spacer further has at least four side walls. Each side wall is positioned 90 degrees away from an adjacent side wall. A microphone is coupled to each side wall of the bezel spacer in such a manner that each microphone is faced 90 degrees with respect to an adjacent microphone to form a compact acoustic array. The end cap is coupled to the second end of the bezel spacer and the electro-optical sensors are mounted to the end cap.

A multi-spectral detection device is provided that includes a housing, a bezel spacer, a microphone, an end cap and electro-optical sensors mounted to the end cap. The housing has an inner chamber that holds sensor components. The bezel spacer has a first end and an opposed, second end. The first end of the bezel spacer extends from a first end of the housing. The bezel spacer has a central bezel passage leading to the inner chamber of the housing. The bezel spacer further has at least four side walls. Each side wall is positioned 90 degrees away from an adjacent side wall. A microphone is coupled to each side wall of the bezel spacer in such a manner that each microphone is faced 90 degrees with respect to an adjacent microphone to form a compact acoustic array. The end cap is coupled to the second end of the bezel spacer and the electro-optical sensors are mounted to the end cap.

Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing and/or eliminating false positive alarm triggers. Detector devices positioned throughout an environment scan for known events that are likely indicative of an alarm condition. A source device that may generate such a sound or image may imprint a fingerprint on to the event that can be detectable by the detector devices but is imperceptible to humans. Additionally, the detector devices may use directionality and known locations of source devices to determine if a triggering event occurred at a known location of a source device. An alarm suppression decision may occur at the detector device and/or relevant information may be sent from the various detectors to a central console for a final alarm decision.