AI Audio Filtering for Misophonia Trigger Sound 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) in headsets or separate units to filter out trigger sounds in real-time by analyzing environmental audio, allowing desired sounds to be heard while blocking undesired ones, using a combination of active noise cancellation and AI-based sound processing.
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 remain ineffective and desired environmental sounds are eliminated
Solution Approach 1:
The audio processing is segmented into different frequency bands, with each band processed independently by dedicated filter banks. This allows selective application of different noise cancellation strategies to different frequency ranges, improving overall effectiveness while preserving desired sounds in each segment
Solution Approach 2:
Different filtering characteristics are applied to different frequency regions. The system uses frequency-dependent filter designs where low-frequency sounds receive one type of processing while high-frequency sounds receive another, optimizing performance for each local frequency region rather than applying a uniform approach
2Object-affected harmful factors
If all environment sound is blocked to solve misophonia, then trigger sounds are completely eliminated, but the individual cannot properly interact with their environment
Solution Approach 1:
The system extracts and removes only the unwanted trigger sounds from the audio stream while leaving the desired environmental sounds intact. This selective extraction approach eliminates the harmful acoustic factors without isolating the user from beneficial environmental audio information
Solution Approach 2:
The audio processing system acts as an intermediary between the environment and the user's ear, selectively filtering trigger sounds while allowing desired sounds to pass through. This intermediary processing layer enables discrimination between harmful and useful sounds rather than blanket blocking
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.