Headset Audio Filtering for Auditory Attention Training
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Solution Overview
Problem
Individuals with conditions such as attention deficit disorder (ADD) or autism face difficulties in processing language due to challenges in filtering out background noise, known as the 'Cocktail Party Effect,' which hinders their ability to focus on a specific voice amidst multiple audio sources.
Innovation Solution
A computer-implemented method using noise-canceling headphones and machine learning to dynamically adjust which background audio sources are filtered, allowing users to improve their auditory attention abilities by optimizing the cocktail of sounds based on their acceptance level, thereby enhancing their capacity to focus on a primary audio source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all background audio sources are filtered out, then the user can focus on the primary audio source, but the user loses the ability to hear and process other important sounds in the environment
Solution Approach 1:
The patent segments background audio sources into different categories (e.g., speech-like sounds, music, environmental noises) and applies different filtering strategies to each category. Important background sounds are preserved while distracting noises are filtered, allowing selective attention without complete isolation.
Solution Approach 2:
The system dynamically adjusts the filtering based on the user's real-time acceptance level. When the user shows discomfort or inability to process information, the system reduces filtering intensity. When the user is comfortable, the system increases filtering to enhance focus, creating a dynamic balance between focus and information access.
2Object-affected harmful factors
If the headset filters all background noise, then the user experiences reduced distractions, but the user cannot naturally process language from multiple audio sources
Solution Approach 1:
The system continuously monitors the user's acceptance level through sensors and adjusts filtering in real-time based on this feedback. This feedback loop ensures that the filtering intensity adapts to the user's actual needs, maintaining both distraction reduction and natural language processing capability.
Solution Approach 2:
The system changes the filtering parameters (e.g., filter threshold, frequency response) based on the user's acceptance level. When acceptance is low, the system reduces filtering intensity; when acceptance is high, it increases filtering. This parameter adjustment allows the system to optimize both distraction reduction and language processing versatility.
3Reliability
If the system provides strong noise filtering, then the user can focus on desired audio sources, but the user may have difficulty processing information from remaining audio sources
Solution Approach 1:
The system applies partial filtering rather than complete filtering. It selectively filters only the most distracting background noises while leaving other background audio sources partially audible. This partial action maintains focus on the primary source while preserving enough background information for contextual understanding.
Data Source
AI summary
Aspects include identifying each of a plurality of audio sources proximate to a user wearing a headset. The audio sources include a primary audio source and a plurality of background audio sources. Aspects include causing the headset to play a set of audio sources to the user by causing each audio source of the set to be unfiltered by the headset. Aspects include determining an acceptance level of the user. Aspects also include determining a background noise filtering adjustment based on the acceptance level and the set of audio sources being played by the headset to the user. Aspects also include causing the headset to adjust a filtering of one or more of the plurality of background audio sources by the headset based on the background noise filtering adjustment.