Alarm System Audio Classification via External Neural Network Verification
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Solution Overview
Problem
Modern alarm systems face challenges in accurately classifying audio events due to increased false alarms, making it difficult to identify correct training data and adapt the system effectively.
Innovation Solution
A method involving a neural network connected to an external unit for re-training, where audio and video data are analyzed to identify event types, with the external unit providing additional intelligence for verification and data transmission, allowing for robust training and efficient communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If the alarm system uses advanced audio detection to identify more event types, then the detection capability is improved, but the false alarm rate increases
Solution Approach 1:
The patent introduces an external unit as an intermediary between the alarm system and the audio events. This external unit performs initial audio analysis and event classification, sending only verified event data to the alarm system. This mediator approach allows the system to detect more event types through the external unit's advanced analysis while maintaining reliability by filtering out false alarms before they reach the alarm system.
Solution Approach 2:
The external unit performs preliminary audio analysis and event verification before data is sent to the alarm system. By conducting initial filtering and classification in advance, the system avoids processing false alarms and reduces the burden on the alarm system's neural network, thereby maintaining high detection capability while improving reliability.
2Measurement precision
If the alarm system collects and processes more training data, then the neural network accuracy is improved, but the training complexity and time increase
Solution Approach 1:
The patent extracts only the essential and verified training data from the external unit and sends it to the alarm system for neural network training. Instead of processing all available audio data, the system selectively extracts high-quality training samples that have been verified by the external unit's analysis, thereby improving neural network accuracy while reducing training complexity and time.
Solution Approach 2:
The patent changes the parameter of training data quality by using verified event data from the external unit rather than raw or unverified audio data. This parameter change ensures that the neural network trains on high-quality, accurate examples, improving convergence and final accuracy while reducing the overall volume of data that needs to be processed.
3Measurement precision
If the alarm system sends all recorded audio to an external unit for analysis, then the event classification accuracy is improved, but the network load and communication requirements increase
Solution Approach 1:
The patent applies partial action by sending only specific portions of recorded audio to the external unit - specifically, segments that contain potential events or are most likely to contain useful training data. Instead of transmitting all recorded audio continuously, the system selectively transmits relevant segments, thereby maintaining high event classification accuracy while significantly reducing network load and communication requirements.
Data Source
AI summary
A method for training an alarm system to classify audio of an event, wherein the alarm system is connected to a neural network trained to classify audio as an event type, the method comprising the steps of: receiving audio recorded during a first period of time; transmitting the audio to an external unit; receiving data from the external unit indicating a sub-period of time of the audio and data indicating an event type of the indicated sub-period of time of the audio; and re-training the neural network by inputting a sub-period of the audio corresponding to the indicated sub-period of time of the audio and using the indicated event type as a correct classification of the sub-period of the audio.