Microphone Phase Signatures for Distinguishing Noise Event Locations
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Solution Overview
Problem
Distinguishing between similar noise events in complex environments using microphones is challenging due to the difficulty in differentiating between similar noise profiles and events of the same type occurring in different locations.
Innovation Solution
Utilizing phase differences between sound samples from multiple microphones to establish a unique noise event signature by exploiting multipath effects, which are transformed into the frequency domain using FFT, and creating a feature vector or matrix to characterize and identify noise events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple microphones are used to detect noise events, then the ability to distinguish between similar events improves, but the complexity of processing and analyzing the data increases
Solution Approach 1:
The patent extracts only the essential feature - phase differences between microphone signals - to characterize noise events. By focusing on this specific extracted feature rather than processing all raw audio data, the system achieves accurate event distinction while reducing processing complexity. The phase difference serves as a compact representation that captures location information without requiring full signal analysis.
Solution Approach 2:
The system performs preliminary characterization of noise events by establishing signatures based on phase differences before actual event detection occurs. This pre-processing step creates reference patterns that simplify subsequent event identification, allowing the system to quickly match new events against pre-established signatures rather than performing complex analysis in real-time.
2Measurement precision
If phase differences between microphones are used to establish noise event signatures, then the ability to identify event location improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces complex mechanical/acoustic measurement systems with signal processing-based phase difference calculation. Instead of using complex hardware to directly measure location, the system uses standard microphone recordings and computes phase differences through mathematical operations. This substitution of mechanical measurement with computational analysis simplifies the physical measurement requirements while maintaining high location 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
Enables accurate identification and characterization of noise events by leveraging multipath interference patterns, enhancing the ability to distinguish between similar events and their locations, thereby improving event detection and management strategies.
Implementation Method 1
closely located microphones will see different destructive and constructive effects by combination of multipath signals
Implementation Method 2
determining phase differences between the sound samples of different microphones
Implementation Method 3
Determining phase differences comprises first transforming the sound samples into the frequency domain, for example by Fast Fourier Transform (FFT)
Data Source
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AI summary
Methods of detecting and characterising a noise event are described, together with apparatus adapted to perform such methods. A method of characterising a noise event, involves locating microphones (21, 22) in an environment and generating a training event at a location (23, 24) in the environment as a reference for a noise event. A sound sample is recorded at each microphone and phase differences between the sound samples are used to establish a noise event signature for an event at that location. A noise event may subsequently be identified by taking sound samples from the microphones associated with the noise event and using phase differences between them to identify the noise event by matching against noise event signatures. A computing system adapted to perform such methods is also described.