Light-Mounted Gunshot Detection Using Edge Audio Analysis
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Solution Overview
Problem
Existing gunshot detection systems face challenges with high bandwidth requirements, processing power consumption, and inaccurate location identification due to echo and obstruction issues, especially in large cities.
Innovation Solution
A network of recording devices mounted on light fixtures uses machine learning and multilateration techniques to process audio data locally and transmit only spectrograms of gunshots to a remote server, reducing bandwidth and accurately determining gunshot locations using machine learning and multilateration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recording devices continuously stream audio data to a remote server for gunshot detection, then gunshot detection capability is improved, but network bandwidth consumption increases significantly
Solution Approach 1:
The patent extracts only the essential information (gunshot detection results and spectrograms) from the continuous audio stream, rather than transmitting the entire audio data. Recording devices perform local analysis using machine learning models to identify gunshots, and only transmit relevant detection data and spectral features to the remote server, dramatically reducing bandwidth requirements while maintaining detection capability
Solution Approach 2:
The patent implements preliminary gunshot detection and analysis at the edge recording devices before data transmission. Machine learning models run locally on each recording device to perform initial gunshot identification, filtering out non-gunsound audio data. This preliminary processing ensures that only relevant information is transmitted to the remote server, optimizing network bandwidth usage
2Area of stationary object
If multiple recording devices are deployed across a city to improve detection coverage, then gunshot detection coverage is improved, but processing power requirements at the remote server increase
Solution Approach 1:
The patent segments the gunshot detection processing tasks between edge recording devices and the remote server. Each recording device independently performs local audio analysis using machine learning models to detect gunshots and generate spectrograms. The remote server then processes only the detected gunshot events from multiple devices, rather than processing all raw audio streams, significantly reducing server processing power requirements while maintaining comprehensive city-wide coverage
Solution Approach 2:
The patent introduces machine learning models as intermediary processing layers at the edge devices. These models act as mediators that filter and pre-process audio data locally, extracting only gunshot-related information before transmission. This intermediary processing layer reduces the computational burden on the remote server by eliminating the need to analyze all raw audio data from multiple recording devices
3Area of stationary object
If recording devices are placed at various locations to increase coverage, then detection coverage is improved, but location accuracy decreases due to echoes and obstructions
Solution Approach 1:
The patent implements feedback mechanisms where the remote server receives detection data from multiple recording devices and uses this information to refine location calculations. The system analyzes timing differences and signal characteristics from multiple sources, using feedback loops to iteratively improve location accuracy by cross-validating data from different recording positions and compensating for environmental factors like echoes and obstructions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid and accurate detection of gunshot locations, allowing authorities to respond promptly and collect evidence effectively.
Implementation Method 1
a microphone inside or mounted to the device housing; and a processor inside the device housing and electrically coupled to the microphone. The processor can be configured to receive audio data from the microphone
Data Source
AI summary
An apparatus for detecting gunshots may include a device housing configured to removably couple to a light fixture; a microphone inside or mounted to the device housing; and a processor inside the device housing and electrically coupled to the microphone. The processor can be configured to receive audio data from the microphone; execute a machine learning model using the audio data as input to determine whether the audio data corresponds to a gunshot; and responsive to determining the audio data corresponds to a gunshot, transmit the audio data to a remote processor.