Noise Discrimination System Using Sound Transfer Functions
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Solution Overview
Problem
Current noise monitoring systems lack the ability to effectively discriminate between noise originating from a specific source and ambient noise, particularly for long-duration events, leading to inaccurate assessments.
Innovation Solution
A system comprising noise monitoring stations, meteorological stations for wind speed and direction data, and a controller that filters transient events, measures sound transfer functions, and compares them to reference functions to determine the probability of noise originating from a specific source, using a combination of low-pass digital filtering and meteorological data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sound level measurement is used to monitor noise from plant activities, then noise levels can be measured and recorded, but the system cannot effectively discriminate between noise originating from the plant and ambient noise from other sources
Solution Approach 1:
The system segments the noise monitoring function into multiple components: a first monitoring station near the plant measures total noise, a second monitoring station at the target location measures received noise, and meteorological stations measure wind conditions. This segmentation allows the system to isolate and attribute noise sources by comparing measurements across different locations and correlating with meteorological data.
Solution Approach 2:
The system introduces meteorological data (wind speed and direction) as an intermediary factor to help discriminate noise sources. By using wind data as a mediator, the system can determine whether noise measured at the target location is likely to have originated from the plant based on the plume dispersion model, thereby resolving the inability to distinguish between plant-generated noise and ambient noise.
2Reliability
If noise monitoring is conducted without filtering transient events, then all noise variations are captured, but transient short-duration events create false positives in noise source attribution
Solution Approach 1:
The system performs preliminary filtering of transient short-duration events from the noise measurements before proceeding with noise source attribution. By removing these transient events in advance, the system prevents them from causing false positives in the later attribution analysis, thereby improving the reliability of noise source identification.
Solution Approach 2:
The system uses feedback mechanisms where the controller continuously receives noise level data from monitoring stations, compares it with meteorological data, and adjusts the noise attribution determination accordingly. This feedback loop allows the system to refine its discrimination between plant-generated noise and ambient noise by continuously evaluating the relationship between measured noise levels and wind conditions.
3Measurement precision
If multiple noise monitoring stations and meteorological stations are deployed, then noise discrimination accuracy is improved, but system complexity increases
Solution Approach 1:
The monitoring stations are designed with multi-functionality to reduce overall system complexity. The same type of monitoring station serves multiple purposes: measuring noise levels, and when combined with meteorological data, determining noise source attribution. This universal approach allows the system to achieve accurate noise discrimination without requiring completely different specialized devices for each function.
Solution Approach 2:
The system merges the noise monitoring and meteorological monitoring functions into an integrated noise discrimination system. By combining data from noise monitoring stations and meteorological stations through a centralized controller that applies plume dispersion modeling, the system achieves accurate noise source attribution while avoiding the complexity of separate independent systems.
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 provides improved accuracy in noise discrimination, reducing false positives and negatives, and ensuring compliance with noise regulations by accurately identifying noise sources, as demonstrated by the comparison with manual filtering methods.
Implementation Method 1
measuring a sound transfer function between the noise source and the target location
Implementation Method 2
measuring wind speed and direction data in an area comprising the noise source and the target location
Data Source
AI summary
A method and a system for noise discrimination, the method comprising measuring noise levels of a noise source, measuring noise levels at a target location positioned at a distance from the noise source, measuring wind speed and direction in an area comprising the noise source and the target location, filtering transient short-duration events from the noise levels measured at the noise source, yielding filtered noise levels of the noise source, filtering transient short-duration events from the noise levels measured at the target location, yielding filtered noise levels of the target location, measuring a sound transfer function between the noise source and the target location using the filtered noise levels, comparing the measured sound transfer function with a reference transfer function; and when the difference between the measured transfer function and the reference transfer function is above a predetermined threshold, determining a probability that noise at the location originates from the noise source.