Active Sound Control for Selective Noise Muting and Source Isolation
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Solution Overview
Problem
Current systems face challenges in classifying and distinguishing sound sources, particularly in noisy environments, where desired sounds are often masked by transient noises and interference, lacking robust automatic classification methods to differentiate human speakers and animals.
Innovation Solution
An automated sound control system uses aural profiles with measurable characteristics to identify and authenticate sound sources, employing deep neural networks and noise modeling to separate and dampen undesired noise in real-time, enhancing the perceptual quality of desired sounds by selectively muting other sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise dampening is applied to remove unwanted sounds, then noise interference is reduced, but desired sounds may also be attenuated
Solution Approach 1:
The system applies different processing treatments to different sound sources based on their characteristics. Desired sounds (human speech, animal sounds) receive enhancement treatment while unwanted noises receive dampening treatment, achieving local differentiation in quality control
Solution Approach 2:
The system continuously monitors acoustic environment and adjusts noise dampening levels based on detected sound sources. The feedback loop ensures that when desired sounds are detected, the dampening is reduced or reversed to preserve them, while maintaining noise reduction for unwanted sounds
2Extent of automation
If automatic sound classification is implemented to distinguish sound sources, then sound source differentiation is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex manual sound classification systems with automated electronic classification using signal processing algorithms. The system automatically identifies and categorizes sound sources (human speech, animal sounds, noises) without mechanical intervention, reducing operational complexity
Solution Approach 2:
The sound classification system is designed to handle multiple types of sound sources simultaneously using a unified algorithmic approach. The same classification framework processes human speech, animal sounds, and various noises, reducing the need for separate specialized systems
3Measurement precision
If deep neural networks are used for sound source identification, then identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary sound classification and filtering before applying deep neural networks. By pre-identifying potential sound sources and filtering obvious noises, the system reduces the computational burden on the neural network, allowing faster processing while maintaining high identification accuracy
Data Source
AI summary
A system automatically controls an electronic device's audio by detecting an active sound source presence within an auditory detection space. The system transitions the electronic device to selectively output a desired sound when the active sound source presence is detected and detects sound in the auditory detection space. The system enhances sound and transforms it into electrical signals. The system converts the electrical signals into a digital signal and identifies active sound segments in the digital signals. The system attenuates noise components in the digital signals and locates the physical location of the active sound source. It adjusts an output automatically by muting a second sound source in a second detection space.