Neural Activity-Guided Audio Scene Modification for Target Clarity
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Solution Overview
Problem
Existing audio systems fail to adapt to a user's auditory attention, leading to inconsistent perception of audio sources in communications or real-world environments.
Innovation Solution
An apparatus and method that measure neural activity to identify the target audio source with the user's attention, modifying audio output or capture by emphasizing or attenuating audio sources based on this identification, using techniques like beamforming and neural network models to enhance clarity and intelligibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If audio output is uniformly distributed to all audio sources, then the system is simple to implement, but the user cannot selectively focus on the target audio source with auditory attention
Solution Approach 1:
The system measures neural activity (e.g., EEG signals) from the user's brain to detect auditory attention, using this feedback to dynamically adjust audio output. The neural activity measurement provides real-time feedback about which audio source the user is attending to, enabling the system to adapt audio distribution accordingly.
Solution Approach 2:
The patent replaces traditional mechanical or manual audio control mechanisms with neural activity-based control. Instead of requiring physical interaction or complex sensor arrays to detect user intent, the system uses electrical neural signals from the brain to automatically determine auditory attention and adjust audio output.
2Measurement precision
If the system emphasizes the target audio source by amplifying its output, then the clarity of the target source improves, but the overall audio scene balance deteriorates
Solution Approach 1:
The system applies different audio processing qualities to different spatial locations or audio sources. The target audio source receives enhanced processing (amplification or beamforming) while other sources maintain their original characteristics, creating local quality differentiation that preserves overall scene balance while improving target clarity.
Solution Approach 2:
The audio emphasis is dynamically adjusted based on real-time neural activity measurements rather than being statically applied. The system continuously monitors which audio source the user is attending to and adaptively modifies emphasis levels, allowing the audio scene composition to remain stable while locally enhancing the target source as needed.
3Measurement precision
If the system uses neural activity measurement to identify target audio sources, then auditory attention detection accuracy improves, but the measurement and processing complexity increases
Solution Approach 1:
The system uses an intermediary processing layer that translates complex neural activity patterns into simplified auditory attention indicators. The neural signals serve as an intermediary between the user's cognitive state and the audio system, requiring sophisticated signal processing to extract meaningful attention information from the raw neural data.
Data Source
AI summary
Various example embodiments are disclosed relating to modifying at least part of an audio scene, for example modifying output and/or capture of at least part of an audio scene based on a measured neural activity of a user. For example, a method may comprise measuring neural activity of a user during output and/or capture of an audio scene and identifying, based on the measured neural activity, at least one target audio source of the audio scene which has the auditory attention of the user. The method may further comprise causing modification of the output and/or capture of at least part of the audio scene based on the identification.