Audio Event Detection via Dynamic Classifier Adaptation
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Solution Overview
Problem
Smart home devices lack an efficient mechanism to differentiate and detect multiple audio events occurring simultaneously, and they do not adjust their programs based on device capabilities and ambient environment variations.
Innovation Solution
An electronic device is equipped with a classifier model obtained from a remote server, which distinguishes specific audio features like a baby cry from ambient noises by statistically analyzing audio data split into frequency bands and time windows, and issuing alerts based on probability values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predetermined audio programs are used to identify audio events independently, then the detection process is simple, but the device cannot differentiate multiple audio events occurring simultaneously and cannot adapt to ambient environment variations
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating the audio event detection model based on ambient environment characteristics. The system adjusts detection parameters and thresholds according to real-time environmental conditions, transforming a static predetermined program into a dynamic adaptive system that evolves with the ambient environment.
Solution Approach 2:
The system employs feedback mechanisms where detection results and environmental characteristics are fed back into the model updating process. This feedback loop enables the system to learn from past detections and environmental variations, continuously improving its ability to differentiate multiple audio events while adapting to changing ambient conditions.
2Measurement precision
If conventional predetermined audio programs are used, then the device complexity is low, but the measurement precision of audio event detection is insufficient in complex ambient environments
Solution Approach 1:
The system performs preliminary actions by pre-processing audio signals and extracting relevant features before detection. It prepares the detection model in advance by training it with ambient environment characteristics, enabling more accurate detection without requiring excessive complexity during real-time operation.
Solution Approach 2:
The patent introduces additional dimensions to the detection process by incorporating temporal and spectral analysis. It transforms the audio signal into multiple feature dimensions (frequency spectrum, time-domain characteristics) and applies multi-dimensional detection, significantly improving accuracy while managing complexity through structured feature extraction.
3Reliability
If the device processes audio data without environmental adaptation, then the processing speed is fast, but the reliability of detection in varying ambient conditions deteriorates
Solution Approach 1:
The system segments the audio processing task into distinct stages: ambient environment characterization, feature extraction, model-based detection, and result validation. This segmentation allows parallel processing of different audio streams and environmental adaptations, maintaining high productivity while improving reliability through specialized processing for each stage.
Solution Approach 2:
The patent introduces an intermediary layer between raw audio input and detection output - a learned detection model that acts as a mediator. This intermediary transforms raw audio features into reliable detection results by filtering out ambient noise patterns and focusing on significant event characteristics, thereby improving reliability without substantially reducing processing efficiency.
Data Source
AI summary
This application discloses a method implemented by an electronic device to detect a signature event (e.g., a baby cry event) associated with an audio feature (e.g., baby sound). The electronic device obtains a classifier model from a remote server. The classifier model is determined according to predetermined capabilities of the electronic device and ambient sound characteristics of the electronic device, and distinguishes the audio feature from a plurality of alternative features and ambient noises. When the electronic device obtains audio data, it splits the audio data to a plurality of sound components each associated with a respective frequency or frequency band and including a series of time windows. The electronic device further extracts a feature vector from the sound components, classifies the extracted feature vector to obtain a probability value according to the classifier model, and detects the signature event based on the probability value.