Headset Audio Filtering for Real-Time Misophonia Trigger Removal
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Solution Overview
Problem
Existing solutions for misophonia, such as active noise cancellation and complete sound blocking, fail to effectively mitigate trigger sounds without compromising the ability to interact with the environment and often eliminate desired sounds, leaving individuals with misophonia anxious and avoiding social situations.
Innovation Solution
A device utilizing machine learning algorithms and deep learning processors (DLPs) to filter out trigger sounds in real-time by integrating active noise cancellation with a headset or separate processing unit, allowing the user to hear filtered audio and white noise, while blocking undesirable sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If active noise cancellation is used to mitigate trigger sounds, then lower frequency sounds are reduced, but higher frequency trigger sounds in unorganized patterns remain ineffective and desired environmental sounds are eliminated
Solution Approach 1:
The system dynamically adapts its noise cancellation profile based on real-time spectral analysis of the environment. Instead of using fixed frequency cancellation, the machine learning algorithm continuously identifies trigger sound patterns across all frequency ranges and adjusts cancellation parameters dynamically, making the system effective against variable higher frequency trigger sounds while preserving desired sounds.
Solution Approach 2:
The invention changes the parameters of noise cancellation by using machine learning to identify and target specific trigger sound characteristics rather than canceling broad frequency ranges. This allows selective cancellation of trigger sounds at any frequency while maintaining desired environmental sounds, resolving the limitation of traditional active noise cancellation.
2Object-affected harmful factors
If complete sound blocking is used to eliminate all trigger sounds, then trigger sounds are fully blocked, but the ability to interact with the environment is prevented
Solution Approach 1:
The system extracts and removes only the harmful trigger sound components from the ambient audio spectrum while leaving desired environmental sounds intact. Using machine learning, it identifies and separates trigger sounds from normal environmental sounds, allowing users to interact with their environment naturally while being protected from specific trigger sounds.
Solution Approach 2:
The invention applies noise cancellation selectively to specific sound sources and frequency patterns identified as triggers, rather than uniformly blocking all sounds. This localized approach allows desired environmental sounds to pass through while targeting only the harmful trigger sounds, preserving environmental interaction.
3Object-affected harmful factors
If traditional noise blocking methods are used, then some trigger sounds are reduced, but desired environmental sounds are also eliminated and social interaction is compromised
Solution Approach 1:
The system uses machine learning algorithms that continuously analyze audio input and provide feedback to adjust noise cancellation in real-time. This feedback mechanism allows the system to learn which sounds are triggers and which are desired environmental sounds, enabling selective cancellation that preserves important environmental information while blocking trigger sounds.
Solution Approach 2:
The machine learning algorithm acts as an intermediary between the raw environmental sounds and the user's perception. It processes and filters sounds intelligently, allowing desired sounds to pass through to the user while blocking trigger sounds, thus preventing loss of important environmental information.
Data Source
AI summary
Systems and methods for treating misophonia include utilizing machine learning within a deep learning processor to allow a user to listen to ambient sounds from their environment without hearing trigger sounds. The method includes the steps of recording ambient sounds with one or more microphones, digitizing the recorded ambient sounds into digital signals, creating spectrographic data for the digital signals, comparing the spectrographic data against a signature library that comprises preprogrammed spectrographic data for the unwanted trigger sounds, identifying the spectrographic data that corresponds to the unwanted trigger sounds, removing the unwanted trigger sounds from the spectrographic data to provide filtered spectrographic data, converting the filtered spectrographic data into a filtered digital signal, converting the filtered digital signal into a filtered audio signal that does not include the unwanted trigger sounds, and playing the filtered audio signal to the user through the one or more speakers on the headset.