Dual-Microphone Snoring Detection for Directional Source Tracking
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Solution Overview
Problem
Existing snoring detection methods rely on single microphones, which fail to determine the direction of snoring and cannot distinguish multiple sources, limiting their effectiveness in shared bed environments.
Innovation Solution
A two-microphone system is used to capture audio at spatially separated locations, employing cross-correlation and Fourier transforms to determine the direction of snoring by analyzing frequency ranges characteristic of snoring sounds, allowing for directional detection and adjustment of bed components to alleviate snoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single microphone is used for snoring detection, then the device complexity is reduced, but the ability to determine direction of snoring and distinguish multiple sources is lost
Solution Approach 1:
The patent transitions from a single-point (0D) microphone to a spatial array (1D/2D) of microphones. By adding spatial dimensionality through multiple microphones positioned at different locations, the system gains the capability to determine direction of arrival and distinguish multiple sound sources while maintaining manageable complexity through structured signal processing.
Solution Approach 2:
The patent divides the audio detection function across multiple independent microphone elements. Each microphone captures sound from its specific position, and the system segments the overall sound field into directional components through signal processing, enabling precise localization and multi-source differentiation.
2Measurement precision
If multiple microphones are used to determine direction of snoring, then direction detection accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical directional indication systems with computational signal processing. Instead of using mechanically complex directional microphones or arrays with moving parts, the system uses standard microphones combined with cross-correlation algorithms and Fourier transforms to achieve precise direction detection, thereby reducing mechanical complexity while maintaining high measurement precision.
3Measurement precision
If cross-correlation and Fourier transforms are used to analyze sound directions, then direction detection accuracy is improved, but the processing time increases
Solution Approach 1:
The patent implements periodic signal processing by applying cross-correlation and Fourier transforms at regular intervals rather than continuously. This periodic approach maintains high direction detection accuracy by analyzing sound field characteristics at sufficient sampling rates while reducing computational burden and processing time compared to continuous analysis.
Solution Approach 2:
The patent applies partial action by focusing computational resources on analyzing only the frequency ranges and time windows most relevant to snoring detection. Rather than processing the entire frequency spectrum continuously, the system concentrates analysis on characteristic snoring frequencies, reducing overall processing time while maintaining detection accuracy.
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
The system effectively identifies the direction of snoring, enabling targeted adjustments to reduce snoring by positioning bed components, even in noisy environments with multiple sound sources.
Implementation Method 1
using a first microphone to convert a first sound into a first electrical signal
Implementation Method 2
using a second microphone to convert a second sound into a second electrical signal
Implementation Method 3
using a Fast Fourier transform to generate a Fourier transform of the first electrical signal and the second electrical signal
Implementation Method 4
cross correlating the first electrical signal and the second electrical signal
Data Source
AI summary
A system for detecting snoring. The system includes a first microphone to convert a first sound into a first signal, a second microphone to convert a second sound into a second signal, and a processor. The processor generates a third signal from the first and second signals that is representative of the first sound arriving at the first microphone and the second sound arriving at the second microphone to select first and second portions of the third signal, and to derive a metric from the second portion of the third signal. The first portion corresponds to the first sound arriving at the first microphone and the second sound arriving at the second microphone. The second portion contains only components of the first portion that have a frequency within a frequency range of interest. The metric indicates if the first portion of the third signal includes a component consistent with snoring.