Noise Pollution Mapping with Machine Learning and Directional Sirens
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Solution Overview
Problem
Current noise pollution measurement systems in urban areas are inadequate, as they fail to capture a broad range of noise sources, adapt to changing environments, and require significant maintenance, while phone-based measurements do not comply with noise measurement standards, leading to incomplete and inaccurate noise pollution maps.
Innovation Solution
A system utilizing machine learning and audio devices like headsets, smart speakers, and computers for initial noise recordings, classification, and aggregation to create comprehensive noise pollution maps, with active noise cancellation for improved audio quality and directional sirens for emergency vehicles to reduce noise pollution and enhance navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor networks are deployed permanently in urban areas for noise measurement, then continuous noise monitoring capability is improved, but device complexity and maintenance requirements increase significantly
Solution Approach 1:
The patent employs machine learning models that automatically classify and process noise data without requiring manual intervention or maintenance. The system self-updates and adapts to changing noise patterns, eliminating the need for periodic maintenance of traditional sensor networks while maintaining continuous monitoring capability.
Solution Approach 2:
The patent replaces physical sensor networks with a software-based machine learning system that runs on existing devices. This substitution eliminates the need for complex hardware deployment and maintenance while achieving continuous noise monitoring through software-based data collection and analysis.
2Ease of operation
If phone-based noise measurement applications are used, then ease of deployment is improved, but measurement precision and compliance with noise measurement standards deteriorate
Solution Approach 1:
The patent introduces machine learning models as an intermediary layer between the smartphone microphone and the noise measurement output. This intermediary processes and calibrates the raw audio data, ensuring compliance with noise measurement standards while maintaining the ease of deployment through mobile applications.
Solution Approach 2:
The patent transforms the measurement parameters by using machine learning to adjust and calibrate the noise measurements based on environmental conditions, device characteristics, and known noise signatures. This parameter transformation ensures standard compliance while maintaining the accessibility of phone-based measurements.
3Ease of operation
If traditional sirens are used for emergency vehicles, then ability to alert public is improved, but noise pollution in urban areas increases
Solution Approach 1:
The patent applies directional sound emission technology that concentrates acoustic energy in specific directions rather than omnidirectional distribution. This allows emergency vehicles to alert the public effectively in the direction of travel while minimizing noise pollution in surrounding areas, achieving local optimization of sound distribution.
Solution Approach 2:
The patent replaces the symmetric omnidirectional siren pattern with an asymmetric directional sound beam that is optimized for the forward direction. This asymmetric sound distribution maintains alerting effectiveness for traffic ahead while reducing noise exposure for pedestrians and residents in other directions.
Data Source
AI summary
Constructing a noise pollution map for an area includes a first subset of users performing initial noise recordings in the area using audio devices, using machine learning to provide classification of noises in the initial noise recordings, a second subset of users, larger than the first subset of users, capturing noise in the area using audio devices, creating summaries of noises using the classification to classify noises captured by the second subset of users, and aggregating the summaries to construct the noise pollution map of the area. The audio devices may include headsets, smart speakers, smart television sets, and/or computers. The summaries of noises may be created using software that is installed locally on devices of the second subset of users. The summaries may include source information, amplitude and frequency characteristics, duration, parameters of a corresponding one of the audio devices, user location, surroundings, and/or user movement information.