Hierarchical Acoustic Detection for Security Threats
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Solution Overview
Problem
Acoustic-based security threat detection systems consume significant network bandwidth and computation resources by transmitting all collected sound data continuously to the processing center, which is inefficient and resource-intensive.
Innovation Solution
A hierarchical acoustic detection system that uses a microphone to capture acoustic signals, processes them to determine the rate of intensity variation, and only transmits data samples indicative of potential security threats to a remote server for further analysis, reducing unnecessary data transmission and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all collected acoustic signals are transmitted continuously to the processing center, then the security threat detection coverage is improved, but the network bandwidth consumption and computation resources increase significantly
Solution Approach 1:
The system performs preliminary analysis of acoustic signals at the edge device before transmission. It calculates features such as sound pressure level, frequency spectrum, and time-domain characteristics, and only transmits signals that exceed predetermined thresholds or show abnormal patterns. This preliminary filtering action reduces the volume of transmitted data while ensuring that potential security threats are not missed.
Solution Approach 2:
The system applies different transmission strategies to different acoustic signal segments based on their local characteristics. Normal ambient sounds are filtered out and not transmitted, while abnormal sounds exceeding thresholds are transmitted for further analysis. This localized quality differentiation ensures that only relevant data consumes network bandwidth.
2Measurement precision
If all collected acoustic signals are transmitted continuously to the processing center, then the security threat detection accuracy is improved, but the computation resources at the edge device increase
Solution Approach 1:
The acoustic signal processing is segmented into two stages: edge processing and cloud processing. At the edge device, basic feature extraction and threshold-based filtering are performed with low computational cost. Only signals that pass the edge filtering stage are transmitted to the cloud for more computationally intensive analysis. This segmentation distributes computation resources efficiently across different levels of the system.
Solution Approach 2:
The system performs partial analysis of acoustic signals at the edge device, focusing only on extracting critical features and comparing them against thresholds. Full-spectrum analysis and complex pattern recognition are reserved for the cloud-based processing center. This partial action at the edge reduces local computation resource consumption while maintaining overall detection accuracy through subsequent cloud-based analysis.
Data Source
AI summary
Systems and methods for detecting a security threat over a network are provided. The system comprises a microphone configured to capture acoustic signals; a hardware interface configured to generate data samples from the acoustic signals; a memory storing a plurality of instructions; and a hardware processor configured to execute the instructions to: determine information indicative of a rate of intensity variation of the acoustic signals; and determine, based on the information, whether to transmit the data samples to a remote server. The hardware processor is also configured to, after determining to transmit the data samples to the remote server, generate data packets that include the data samples, and transmit the data packets to the remote server. The remote server can then reconstruct the data samples from the data packets and, if the data samples indicates a security threat, transmit a warning signal to a monitoring device.