Sound Frame Analysis for Real-Time Event Detection
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Solution Overview
Problem
Current audio-based surveillance systems are ineffective in real-time event detection, often resulting in false negatives and require extensive training to differentiate between characteristic and uncharacteristic sounds, without considering contextual factors like time, location, or pre-existing knowledge, and they do not utilize mobile sound sources effectively.
Innovation Solution
A computer-implemented system that includes a receiver for sensor input, a frame analyzer to create and compare sound frames, a context builder to associate contextual information, a rule builder to process temporal sound frames, and an alert generator to flag events, with a central server for continuous learning and storage of sound signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pattern matching with reference database is used for sound-based surveillance, then the system can identify recurring sounds, but it produces false negatives and cannot detect events in real-time
Solution Approach 1:
The system performs preliminary analysis by creating sound frames and comparing them against learned patterns before final event determination, enabling real-time detection while maintaining accuracy through pre-processing and contextual evaluation
Solution Approach 2:
The system dynamically adapts its detection thresholds and patterns based on learned environmental contexts, allowing it to adjust sensitivity in real-time while maintaining reliable event detection across varying conditions
2Measurement precision
If extensive training is provided to differentiate characteristic and uncharacteristic sounds, then detection accuracy improves, but system complexity and training requirements increase
Solution Approach 1:
The system performs self-learning by automatically analyzing environmental sounds and building contextual patterns without external intervention, eliminating the need for extensive manual training while achieving high differentiation accuracy
Solution Approach 2:
The system uses feedback from continuous sound analysis to refine its patterns and improve differentiation accuracy over time, automatically adapting to new sound types and contexts without requiring retraining
3Measurement precision
If contextual information such as time, location, and pre-existing knowledge is integrated, then event identification accuracy improves, but processing complexity increases
Solution Approach 1:
The system segments contextual information into distinct categories (time, location, pre-existing knowledge) and processes each separately before integration, reducing processing complexity while maintaining high event identification accuracy
Solution Approach 2:
The system uses sound frames as an intermediary representation that encapsulates contextual information, simplifying the integration process by providing a standardized format for combining multiple contextual data sources
Data Source
AI summary
A system and method to detect an event by analyzing sound signals received from a plurality of configured sensors. The sensors can be fixed or mobile and sensor activity is tracked in a sensor map. The frame analyzer of the system compares sound signals received from the sensors and applies knowledge data to determine if any deviation observed can be determined to be an uncharacteristic event. A rule data set comprising priority data, type of event, location is applied to the output of the frame analyzer to determine if the uncharacteristic sound observed is an event. On detection of an event, alerts are issued to appropriate authority. Further, sound frame and contextual data associated with the event are stored to serve as continuous learning for the system.