Neural-Attention Audio Output Control for Real-World Sound Awareness
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Solution Overview
Problem
Existing audio output devices struggle to dynamically adjust audio output based on a user's auditory attention between internal audio data and external real-world sounds, often requiring manual intervention or inefficient noise cancellation modes.
Innovation Solution
An apparatus and method that utilize neural activity measurement to identify which audio, internal or external, has a user's attention, and adjust audio output accordingly, including noise cancellation and sound capture beam steering, to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used to switch between internal audio data and external real-world sounds, then the user can control audio output, but the operation complexity increases and user convenience decreases
Solution Approach 1:
The system automatically detects user auditory attention through neural activity measurement and autonomously switches between internal audio data and external real-world sounds without requiring manual user intervention. The apparatus serves itself by using the user's own neural signals as the control input, eliminating the need for manual operation while maintaining intuitive control.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where neural activity measurements are continuously monitored, processed to determine auditory attention, and used to dynamically adjust audio output. This real-time feedback loop enables automatic adaptation to user needs without manual intervention.
2Object-affected harmful factors
If noise cancellation mode is used to block external sounds, then external noise is reduced, but the user loses awareness of real-world audio environment
Solution Approach 1:
The system dynamically adjusts the balance between noise cancellation and environmental awareness based on real-time detection of user auditory attention. When the system detects that the user is attending to external sounds, it automatically reduces noise cancellation intensity or switches to transparency mode, allowing natural sound passage. This dynamic adaptation resolves the contradiction by making the noise cancellation level variable rather than fixed.
Solution Approach 2:
The system changes operational parameters (noise cancellation intensity, microphone gain, speaker output levels) based on detected neural activity patterns. When external attention is detected, parameters are adjusted to prioritize environmental sound transmission while maintaining protection from harmful noises, thus preserving real-world audio awareness while still providing selective noise management.
3Extent of automation
If the system continuously monitors neural activity to identify auditory attention, then audio output can be automatically adjusted, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system uses periodic sampling of neural activity at strategically chosen intervals. The monitoring is activated based on detected states (e.g., when audio playback is detected or when environmental sounds are present), allowing the system to balance automation with energy conservation by not continuously consuming power for neural signal acquisition and processing.
Solution Approach 2:
The system applies different monitoring intensities to different operational contexts. During periods when internal audio playback is active, neural monitoring focuses on detecting attention to that specific audio source. When environmental sounds are predominant, monitoring shifts to detect external attention. This localized approach to monitoring reduces overall energy consumption while maintaining effective automatic adjustment capability.
Data Source
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AI summary
Example embodiments relate to an apparatus, method and computer-program product relating to controlling output of audio data. An example method is disclosed, comprising outputting a first set of audio data via one or more loudspeakers, capturing, via one or more microphones, a real-world audio scene to provide a second set of audio data for output via the one or more loudspeakers and identifying which of the first set of audio data and at least part of the real-world audio scene has the auditory attention of the user based on a measured neural activity of the user. The method may also comprise controlling output of at least some of the first and/or second set of audio data via the one or more loudspeakers based on the identification.