User-Feedback Sound Classification for Comprehensive Premises Monitoring
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Solution Overview
Problem
Existing premises security systems have blind spots due to limited sensor fields of view and detectable characteristics, and existing sound sensors lack comprehensive monitoring capabilities.
Innovation Solution
A premises monitoring system using a microphone to detect sounds, a processor to generate classification data, and a classification model trained to recognize expected and unexpected sounds based on environment data, with the ability to update the model using user identification data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized sensors (motion sensor, video analytics-based sensor) are used for premises monitoring, then specific events can be detected, but blind spots exist due to limited field of view and detectable characteristics
Solution Approach 1:
The patent combines multiple sensor types (microphone for acoustic detection, motion sensor, video analytics-based sensor) into a unified monitoring system. The processor integrates data from all sensors to create comprehensive monitoring coverage, eliminating blind spots by merging the detection capabilities of each sensor type.
Solution Approach 2:
The monitoring system is designed with multi-functionality to detect various types of events through different sensors. The processor performs multiple functions including analyzing acoustic patterns, detecting motion, and processing video analytics data, making the system universally applicable to different monitoring scenarios without requiring separate specialized systems.
2Reliability
If sound sensors are used to detect noises, then acoustic events can be monitored, but the system lacks comprehensive monitoring capabilities and cannot distinguish between expected and unexpected sounds
Solution Approach 1:
The system dynamically adapts its sound detection capabilities by learning from environmental data and user feedback. The processor continuously updates the classification model with new information about expected sounds in different environments, enabling the system to adapt to changing acoustic profiles while maintaining high detection accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where users can provide identification data for unrecognized sounds, and the processor uses this feedback to update the classification model. This feedback loop continuously improves the system's ability to distinguish between expected and unexpected sounds across different environments.
3Measurement precision
If a classification model is trained to recognize sounds, then sound classification can be performed, but the model requires continuous updates to maintain accuracy with new environmental data
Solution Approach 1:
The system performs preliminary actions by collecting and storing environmental data and user feedback in advance. The processor maintains a database of expected sounds and classification examples, allowing the model to be updated efficiently without requiring extensive real-time training data collection during operational use.
Solution Approach 2:
The system discards outdated or inaccurate classification data and recovers useful patterns from new environmental observations and user feedback. The processor selectively updates the classification model by incorporating only the most relevant new information, maintaining accuracy while minimizing the time required for model maintenance.
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
Enhances comprehensive sound monitoring by identifying unexpected sounds and updating the classification model to improve accuracy, addressing sensor limitations and enhancing security.
Implementation Method 1
a microphone that detects a sound and generates audio data based on the sound
Data Source
AI summary
A method of training a sound classification model for a premises monitoring system may include receiving audio data corresponding to a sound detected at a premises. The audio data may be provided to an active instance of a sound classification model, which may generate classification data indicating the sound is unrecognized. The audio data may be provided to a user device, and updated classification data indicating an identity of the sound may be received from the user device. The audio data and the updated classification data may be stored in a data bucket corresponding to the identity of the sound. When the data bucket contains a threshold quantity of user-classified audio data, a new instance of the sound classification model may be generated and retrained using the user-classified audio data. The active instance of the classification model may be replaced with the new instance of the sound classification model.