Abnormal Sleep Audio Recognition via Snore Intensity Analysis
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Solution Overview
Problem
Current sleep monitoring methods using smartphones are not accurate enough to recognize abnormal sleep audio clips, as they rely solely on output results from models without considering snore information before and after the target audio clip.
Innovation Solution
A method that involves obtaining initial audio clips from sensors, determining a target audio clip matching a preset abnormal sleep state, calculating a confidence value based on snore information before and after the target audio clip, and using this value to accurately identify abnormal sleep audio clips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sleep monitoring is performed using a smartphone with a general model, then the ease of operation is improved, but the measurement precision deteriorates
Solution Approach 1:
The patent changes the parameters used for sleep monitoring by incorporating snore intensity information from audio clips alongside model output results. This multi-parameter approach improves measurement precision while maintaining the ease of using a smartphone for monitoring
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes both the model output results and raw audio snore information. This intermediary processing step enhances measurement precision by combining multiple data sources before reaching the final monitoring conclusion
2Device complexity
If only model output results are used for sleep monitoring, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The patent segments the sleep monitoring process into two independent parts: model output result analysis and audio snore information analysis. This segmentation allows adding measurement precision improvements without increasing overall device complexity, as each segment can be processed separately
3Measurement precision
If snore information before and after target audio clip is analyzed, then the measurement precision is improved, but the loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing audio clips during sleep periods. When abnormal breathing is detected, the system can quickly retrieve and analyze the pre-captured snore information without requiring real-time processing, thus improving measurement precision without significant time loss
Data Source
AI summary
A method for recognizing an abnormal sleep audio clip, includes: obtaining a plurality of initial audio clips collected by a sensor, and determining a target audio clip matching a preset sleep state from the initial audio clips; determining first snore information before the target audio clip and second snore information after the target audio clip based on the initial audio clips; determining a confidence value for the target audio clip based on the first snore information and the second snore information; and determining whether the target audio clip is the abnormal sleep audio clip based on the confidence value of the target audio clip.