Acoustic Event Detection Model with Autonomous Feedback
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Solution Overview
Problem
Existing systems lack the capability to remotely detect and respond to acoustic events, such as a child's cry, without a user's presence, and require manual adjustments to improve detection accuracy over time.
Innovation Solution
A computer-implemented method using a machine-learned acoustic detection model that analyzes audio data to detect events and provides notifications with response options, allowing remote activation of peripheral devices to address the event, with the model automatically re-training based on user feedback to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of detection parameters is used to improve accuracy, then detection precision improves, but user time loss increases
Solution Approach 1:
The system automatically adjusts detection parameters and re-trains models based on feedback data without requiring manual user intervention. The computing system performs self-optimization by analyzing detection accuracy metrics and autonomously updating model parameters, thereby maintaining high detection accuracy while eliminating time loss associated with manual adjustments.
Solution Approach 2:
The system implements a feedback loop where detection results are evaluated against ground truth data, and the model is re-trained using this feedback to continuously improve accuracy. This automated feedback mechanism allows the system to maintain high detection precision without requiring manual parameter tuning, thus resolving the contradiction between accuracy and time investment.
2Ease of operation
If remote acoustic event detection is implemented, then user convenience improves, but detection reliability may worsen
Solution Approach 1:
The system replaces manual acoustic monitoring with an automated machine-learning-based detection system. The computing system processes audio data from remote sources using trained models to identify acoustic events, providing reliable remote detection without requiring physical user presence. This substitution maintains high reliability while significantly improving user convenience.
Solution Approach 2:
The system performs preliminary actions by pre-training acoustic detection models on diverse audio data before deployment. The models are prepared in advance to recognize various acoustic events, ensuring reliable detection performance when deployed in remote scenarios. This preliminary preparation enables the system to maintain high reliability while operating remotely, resolving the contradiction between convenience and reliability.
Data Source
AI summary
Systems and methods for detecting, classifying, and correcting acoustic (waveform) events are provided. In one example embodiment, a computer-implemented method includes obtaining, by a computing system, audio data from a source. The method includes accessing, by the computing system, data indicative of a machine-learned acoustic detection model. The method includes inputting, by the computing system, the audio data from the source into the machine-learned acoustic detection model. The method includes obtaining, by the computing system, an output from the machine-learned acoustic detection model. The output is indicative of an acoustic event associated with the source. The method includes providing, by the computing system, data indicative of a notification to a user device. The notification indicates the acoustic event and response(s) for selection by a user. The computing system, via a continuously learned hierarchical process, may initiate autonomous actions in an effort to halt or otherwise modify the acoustic event.