Embedded Audio Sensing With Edge DSP for Private Event Detection
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Solution Overview
Problem
Existing audio sensors face challenges in accurately monitoring environments due to high memory and processing requirements, and they often raise privacy concerns by indiscriminately uploading detected sounds to the cloud.
Innovation Solution
Embedded sensors utilize environmental classifiers to process audio data on the edge, employing DSPs and statistical classifiers optimized for specific environments, reducing the need for cloud-based analysis and ensuring privacy by processing data locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based audio analysis is used to monitor environments, then detection capability is provided, but user privacy is compromised and data security risks increase
Solution Approach 1:
The patent extracts the audio analysis function from the cloud and implements it locally on the embedded sensor device. The DSP performs environmental classification and event detection using stored statistical models without transmitting raw audio data to external servers, thereby eliminating privacy violations while maintaining detection capability
Solution Approach 2:
The patent introduces a local DSP and statistical model library as intermediaries between the audio detector and any potential cloud services. These intermediaries process all audio data locally, acting as a privacy barrier that prevents direct exposure of raw audio data to external systems while still enabling sophisticated environmental classification
2Measurement precision
If comprehensive audio monitoring is implemented to accurately identify sounds, then detection accuracy improves, but memory and processing requirements increase
Solution Approach 1:
The patent applies local quality by customizing the statistical models to match specific environmental contexts. Instead of using a single generic detection algorithm, the system selects and applies environment-specific statistical models (e.g., kitchen vs. bathroom sound profiles) that optimize detection accuracy for each local context, reducing the need for comprehensive processing capabilities
Solution Approach 2:
The patent performs preliminary environmental classification before detailed event detection. The system first determines the environment type using statistical models, then selects the appropriate detection parameters and models for that specific environment. This preliminary classification step enables more efficient and accurate sound identification without requiring comprehensive processing for all possible scenarios
3Reliability
If multiple sensors are added to improve event detection accuracy, then detection reliability improves, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by using a single DSP to perform multiple tasks: environmental classification, event detection, probability calculation, and rule-based filtering. The DSP processes data from various sensor types (audio detectors, cameras, thermometers, humidity detectors, weight scales, vibration sensors) through a unified processing architecture, reducing the need for separate dedicated processing units for each sensor type
Data Source
AI summary
An embedded sensor can include an audio detector, a digital signal processor, a library, and a rules engine. The digital signal processor can be configured to receive signals from the audio detector and to identify the environment in which the embedded sensor is located. The library can store statistical models associated with specific environments, and the digital signal processor can be configured identify specific events based on detected sounds within the particular environment by utilizing the statistical model associated with the particular environment. The DSP can associate a probability of accuracy for the identified audible event. A rules engine can be configured to receive the probability and transmit a report of the detected audible event.