Adaptive Acoustic Sensing for Localized Factory Noise Profiles
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Solution Overview
Problem
Existing acoustic sensing technologies in industrial IoT settings face challenges in addressing nonuniform noise profiles and require localized parameter settings, complicating installations and reducing the effectiveness of noise reduction methods.
Innovation Solution
The implementation of adaptive noise reduction systems that capture local sound noise environments or noise fingerprints, allowing for automatic application of these profiles to streaming noise reduction in signal processing, eliminating the need for manual tuning and enhancing acoustic data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional noise reduction methods (beamforming, auto encoder, noise cancellation with machine learning) are used, then noise reduction capability is improved, but device complexity and installation complexity increase due to localized parameter settings
Solution Approach 1:
The system automatically captures local sound noise environments and adapts noise reduction parameters without manual intervention. The acoustic sensor system self-configures by listening to the environment and automatically adjusting beamforming weights and noise cancellation parameters, eliminating the need for manual localized parameter settings while maintaining noise reduction effectiveness
Solution Approach 2:
The system performs preliminary environmental scanning and noise profile capture during installation phases, building a baseline understanding of the acoustic environment before actual monitoring begins. This preliminary action allows the system to pre-configure noise reduction parameters based on captured noise fingerprints, reducing the need for complex manual setup
2Area of stationary object
If acoustic sensors are deployed in factory shop floors with multiple machines, then monitoring coverage is improved, but noise from multiple machines changes in time series making noise reduction less effective
Solution Approach 1:
The system dynamically adapts noise reduction parameters in real-time based on changing acoustic environments. It continuously monitors noise profiles from multiple machines and automatically adjusts beamforming weights and filtering parameters to track time-varying noise characteristics, maintaining effectiveness as machines start and stop
Solution Approach 2:
The system captures and stores noise fingerprints of individual machines during quiet periods or when machines operate independently. These pre-captured noise profiles serve as reference templates that the system later uses to identify and reduce specific machine noises during complex multi-machine operations
3Measurement precision
If manual localized parameter settings are required for acoustic sensors, then noise reduction precision is improved, but productivity and ease of operation decrease
Solution Approach 1:
The system automatically performs environmental scanning, noise profile capture, and parameter optimization without requiring manual tuning. The acoustic sensor system self-configures by analyzing the acoustic environment and automatically setting beamforming weights and noise cancellation parameters, achieving precision deployment without manual intervention
Solution Approach 2:
The system replaces manual mechanical adjustment processes with automated electronic adaptation. Instead of requiring technicians to physically adjust sensor positions and manually tune parameters, the system uses electronic signal processing and automated algorithms to achieve optimal noise reduction, dramatically improving deployment productivity
Data Source
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AI summary
Systems and methods involving integrating camera and acoustic sensor data, and automatically capturing the acoustic sensor heatmap for the holistic sensing systems in Internet of Things (IoT) systems. In particular, example implementations described herein capture the local sound noise environment or localized noise profiles (e.g., noise fingerprint) adaptively to the change of noise profiles and automatically apply captured noise profiles to the streaming noise reduction in signal processing for industrial IoT areas.