Neural-Network Active Acoustic Control for Unknown Noise Sources
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Solution Overview
Problem
Existing Active Noise Control (ANC) systems face challenges in effectively reducing unwanted noise without prior knowledge of noise sources or acoustic patterns, and they often require complex setups and precise calibration.
Innovation Solution
A Neural-Network (NN) based Active Acoustic Control (AAC) system that uses a controller with acoustic sensors and transducers to generate counter-noise patterns dynamically, adjusting in real-time to reduce noise within a defined zone without prior information about noise sources or patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ANC systems are used, then noise reduction can be achieved, but the system requires complex setup and precise calibration
Solution Approach 1:
The system employs self-calibration through automated acoustic measurements and neural network training. The controller automatically identifies noise sources, characterizes acoustic paths, and trains the neural network model without requiring manual calibration by users, making the system self-configuring and eliminating complex setup procedures
Solution Approach 2:
The system dynamically adjusts operational parameters including noise cancellation strength, frequency ranges, and zone definitions based on real-time acoustic environment analysis. The neural network continuously adapts its parameters through online learning, allowing the system to optimize performance automatically without manual intervention
2Reliability
If traditional ANC systems are used, then noise reduction can be achieved, but prior knowledge of noise sources or acoustic patterns is required
Solution Approach 1:
The system performs preliminary acoustic characterization by automatically identifying noise sources and mapping acoustic paths before active noise cancellation begins. The neural network is pre-trained with this characterized data, enabling the system to handle various noise types without requiring prior specific knowledge of the noise sources
Solution Approach 2:
The system continuously monitors the acoustic environment using microphones and sensors, comparing actual noise patterns against the neural network model predictions. This feedback loop enables real-time adaptation and retraining, allowing the system to automatically adjust to new or changing noise sources without manual reconfiguration
3Ease of operation
If NN-based AAC system is used, then setup processes are simplified, but computational complexity increases
Solution Approach 1:
The neural network is divided into specialized modules including acoustic path characterization networks, noise source identification networks, and active noise control prediction networks. Each module handles specific computational tasks independently, distributing the computational load and enabling parallel processing that reduces overall computational complexity
Solution Approach 2:
The system implements dynamic computational allocation where the neural network adjusts its processing intensity based on environmental conditions. During stable conditions, the system uses pre-trained models with lower computational demand, while during changing conditions, it activates intensive retraining modes only when necessary, optimizing the balance between performance and computational resources
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 NN-based AAC system efficiently reduces noise in real-time, creating a quiet zone by generating adaptive counter-noise patterns, improving noise reduction efficiency and simplifying setup processes.
Implementation Method 1
an acoustic transducer configured to generate the acoustic control pattern based on the sound control signal
Implementation Method 2
a plurality of acoustic sensors configured to sense a monitoring acoustic information
Implementation Method 3
Active Noise Control (ANC) is a technology using digitally generated noise to reduce unwanted noise. It is based on the principle of superposition of sound waves
Data Source
AI summary
For example, a controller of an Active Acoustic Control (AAC) system may be configured to process input information including AAC configuration information, and a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations. For example, the controller may be configured to process the input information to determine a sound control pattern to control sound within a sound control zone based on the plurality of noise inputs. For example, the controller may include a Neural-Network (NN) trained to generate an NN output based on an NN input, wherein the NN input is based on the AAC configuration information. For example, the controller may be configured to generate the sound control pattern based on the NN output, and to output the sound control pattern to one or more acoustic transducers.