Open-Field Active Noise Cancellation with Deep Learning Adaptation
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Solution Overview
Problem
Existing active noise cancellation (ANC) systems are limited in open spaces, particularly for moving users, and ineffective for high-frequency noises in environments like highways or airplanes, as they rely on fixed speaker arrays that create zones of constructive and destructive interference.
Innovation Solution
A device using a signal processing module with a deep learning framework, specifically a generative adversarial network, processes data from microphones to predict and generate inverse sound wavefronts based on geographical and audio features, allowing adaptable noise cancellation in open spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed speaker arrays are used for noise cancellation, then noise reduction is achieved at specific locations, but the system becomes ineffective for moving users and creates zones of alternating constructive and destructive interference
Solution Approach 1:
The patent implements dynamic adaptation by using multiple microphones to track the user's position and movement in real-time. The system continuously updates the noise cancellation signal based on the user's current location, transforming the static fixed speaker array into a dynamic system that adapts to moving users. This resolves the contradiction by making the noise cancellation effective for moving users while maintaining the fixed speaker array infrastructure.
Solution Approach 2:
The system employs feedback mechanisms by using multiple microphones to monitor the user's position and the acoustic environment continuously. This feedback information is processed to adjust the noise cancellation signal in real-time, ensuring that the destructive interference zones remain aligned with the user's current position rather than being fixed in space. This resolves the contradiction between fixed infrastructure and moving user adaptability.
2Use of energy by moving object
If transducers are located at the user's ear for lower power cancellation, then noise reduction is effective for a single user, but noise cancellation at other locations becomes difficult due to three-dimensional wavefront interference patterns
Solution Approach 1:
The patent segments the noise cancellation task by using multiple fixed speaker arrays distributed across different locations. Each speaker array handles noise cancellation for its local area, and the system collectively provides broad coverage. This segmentation allows the system to maintain low power per transducer while expanding the effective coverage area beyond a single user location.
Solution Approach 2:
The system transitions from a single-point noise cancellation approach to a distributed spatial network of speaker arrays. By adding the spatial dimension with multiple fixed locations, the system achieves both low power consumption at each transducer and broad area coverage collectively, resolving the contradiction between localized effectiveness and extended coverage.
3Adaptability or versatility
If deep learning frameworks with geographical features are used, then noise cancellation adapts to varying locations and frequencies, but the device complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning framework with geographical features and acoustic characteristics of different locations before deployment. This pre-processing of location-specific data allows the system to adapt quickly to new environments without requiring complex real-time processing, reducing the computational burden during actual noise cancellation operations while maintaining high adaptability.
Solution Approach 2:
The system replaces complex real-time acoustic analysis and adaptive signal processing with a pre-trained deep learning model that has already learned the relationships between geographical features and optimal noise cancellation parameters. This substitution of mechanical/computational processing with a trained intelligence model reduces device complexity while maintaining or enhancing adaptability to different locations and frequencies.
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
Enables effective noise cancellation across varying locations and frequencies by generating inverse sound wavefronts that adapt to user movement, providing consistent noise reduction regardless of the user's position.
Implementation Method 1
A noise-cancellation speaker emits a sound wave with the same amplitude but with inverted phase (also known as antiphase) to the original sound. The waves combine to form a new wave, in a process called interference, and effectively cancel each other out—an effect which is called destructive interference.
Data Source
AI summary
The present invention provides a device for actively cancelling a target sound wavefront in an open space, the device comprising a signal processing module comprising at least one processor operatively coupled with a datastore, the at least one processor configured to: receive a data comprising one or more geographical features, and one or more audio features generated by one or more receiving microphones having a geographical relationship with an array of receiving microphones in an area adjacent to a user; process aid data using a prediction model adapting a trained deep learning framework; and provide output the inverse sound wavefront of the target sound at the area of said predicting microphones.