Active Acoustic Control Using Real-Time Noise Feedback
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Solution Overview
Problem
Current Active Noise Control (ANC) systems in vehicles struggle to effectively reduce noise without prior knowledge of noise sources or patterns, and they often require complex setups and adaptations to maintain optimal noise reduction.
Innovation Solution
An Active Acoustic Control (AAC) system that uses a controller with acoustic sensors and transducers to generate a sound control pattern based on real-time noise inputs, adapting to changes in vehicle conditions without prior information about noise sources, and dynamically adjusts settings to optimize noise reduction within a defined zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ANC systems are used without prior knowledge of noise sources, then noise reduction effectiveness deteriorates, but system complexity and adaptation requirements increase
Solution Approach 1:
The AAC system automatically identifies noise sources and adapts to changing acoustic environments without requiring manual configuration or prior knowledge. The system self-adjusts by continuously monitoring acoustic conditions and dynamically modifying control parameters, eliminating the need for complex pre-setup procedures while maintaining effective noise reduction
Solution Approach 2:
The system employs continuous feedback mechanisms where acoustic sensors monitor the acoustic environment and provide real-time data to the controller. This feedback loop enables the system to automatically detect noise sources, assess their characteristics, and adjust control parameters dynamically, resolving the contradiction between effectiveness without prior knowledge and system complexity
2Reliability
If ANC systems continuously adapt to changing vehicle conditions, then noise reduction performance is maintained, but computational requirements and processing time increase
Solution Approach 1:
The system pre-identifies potential noise sources and prepares control strategies in advance based on typical vehicle operating conditions. By anticipating common noise scenarios and having pre-computed control parameters ready, the system can quickly adapt to changing conditions without requiring extensive real-time computation, thus maintaining performance while reducing adaptation time
Solution Approach 2:
The AAC system dynamically adjusts its adaptation rate and computational intensity based on the rate of change in acoustic conditions. When conditions change rapidly, the system increases adaptation frequency; when conditions are stable, it reduces processing intensity. This dynamic approach maintains noise reduction performance while optimizing computational efficiency and reducing unnecessary processing time
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 AAC system effectively reduces noise in vehicles by generating a sound control pattern that adapts to changing conditions, providing an improved driving experience by minimizing unwanted noise without the need for prior knowledge of noise sources or complex setups.
Implementation Method 1
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. Generally, sound is a wave, which is traveling in space. If another, second sound wave having the same amplitude but opposite phase to the first sound wave can be created, the first wave can be totally cancelled.
Data Source
AI summary
For example, a controller of an Active Acoustic Control (AAC) system may be configured to process input information, the input information including AAC configuration information corresponding to a configuration of AAC in a sound control zone; a plurality of noise inputs representing acoustic noise at a plurality of noise sensing locations; and a plurality of residual-noise inputs representing acoustic residual-noise at a plurality of residual-noise sensing locations within the sound control zone. For example, the controller may determine a sound control pattern to control sound within the sound control zone based on the AAC configuration information, the plurality of noise inputs, and the plurality of residual-noise inputs. For example, the controller may output the sound control pattern to a plurality of acoustic transducers.