Audio Fourier Analysis for Physical-Barrier Exposure Quantification
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Solution Overview
Problem
Existing contact tracing technologies, particularly those using Bluetooth®, suffer from high false positive rates due to their inability to differentiate between proximity and actual exposure through physical barriers, leading to unnecessary isolation measures and economic costs.
Innovation Solution
The method involves recording and analyzing audio data using Fourier transforms to calculate a cross-correlation score, which is then compared to a threshold to determine actual exposure risk, accounting for environmental differences caused by physical barriers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Bluetooth proximity detection is used for contact tracing, then contact tracking capability is improved, but false positive rate increases due to inability to differentiate proximity from actual exposure through physical barriers
Solution Approach 1:
The patent introduces audio data as an intermediary medium to verify actual exposure. Instead of relying solely on Bluetooth proximity signals, the system uses recorded audio data and its Fourier transforms as a mediator to determine whether physical barriers existed during proximity events, thereby reducing false positives while maintaining contact tracking capability
Solution Approach 2:
The patent replaces the purely electromagnetic Bluetooth detection system with an acoustic-based verification system. By substituting radio frequency proximity detection with audio recording and spectral analysis, the system can distinguish between mere proximity and actual exposure through physical barriers, improving measurement precision without compromising reliability
2Measurement precision
If audio data recording and Fourier transform analysis is implemented, then exposure detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by continuously recording audio data and computing Fourier transforms in advance, storing them in memory for later analysis. This allows the system to quickly determine exposure status by retrieving pre-computed spectral data rather than performing real-time analysis during proximity events, thereby improving detection accuracy while managing device complexity through advance preparation
Solution Approach 2:
The patent creates spectral copies of audio data through Fourier transforms. Instead of analyzing raw audio signals directly, the system works with frequency domain representations that capture essential exposure characteristics in a compressed form. These spectral copies enable efficient comparison and analysis while reducing the computational burden of processing raw audio data
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces false positives by accurately distinguishing between proximity and actual exposure, enhancing the reliability and efficiency of contact tracing systems.
Implementation Method 1
performing a first Fourier transform on the recorded audio data
Implementation Method 2
computes a cross correlation of the Fourier transforms and finding a maximum of the cross correlation that constitutes an exposure score
Data Source
AI summary
The method comprises a first device recording audio data responsive to a wireless proximity contact event with a second device and performing a first Fourier transform on the recorded audio data. The first device sends the first Fourier transform to the second device and receives a second Fourier transform of audio data recorded concurrently by the second device in response to the wireless proximity contact event. The first device, computes a cross correlation of the Fourier transforms and finding a maximum of the cross correlation that constitutes an exposure score. The first device compares the exposure score to a specified threshold that determines whether or not users of the first and second devices have had unacceptable exposure to each other and outputs the exposure score and a binary threshold result.