Auditory Device Sound Modification Through Real-Time Audio Classification
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Solution Overview
Problem
Current auditory devices require manual adjustment of sound settings, which is time-consuming and can lead to hearing damage and discomfort, and existing machine-learning models for audio classification are too resource-intensive and slow to react in real-time.
Innovation Solution
A large language model is used to classify audio sources as positive or negative based on user input, allowing real-time adjustment of sound settings in auditory devices, such as hearing aids, by amplifying positive sources and reducing or canceling negative sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of sound settings is used, then user can control audio preferences, but it is time-consuming and requires continuous user intervention
Solution Approach 1:
The auditory device automatically adjusts audio settings by classifying environmental sounds and applying user-defined preferences without requiring manual intervention. The system serves itself by continuously monitoring the environment and autonomously modifying sound output based on stored user profiles and real-time audio classification.
2Productivity
If existing machine-learning models are used for audio classification, then audio can be categorized, but the models are too resource-intensive and slow for real-time processing
Solution Approach 1:
The audio processing system is divided into separate functional components: audio capture by microphones, initial processing and feature extraction, classification by the processor, and output modification. This segmentation allows each component to operate efficiently within its optimal performance range, enabling real-time processing with reduced resource consumption.
3Object-affected harmful factors
If loud noises are not filtered, then all audio is preserved, but user suffers hearing damage and discomfort
Solution Approach 1:
The system continuously monitors environmental audio through microphones, processes the sound data through machine learning classification, and automatically adjusts output based on user preferences and safety thresholds. This closed-loop feedback mechanism protects hearing by reducing harmful sounds while maintaining user comfort and preserving desirable audio elements.
Data Source
AI summary
A method includes receiving input from a user that identifies types of audio as positive audio or negative audio. The method further includes outputting, with a large language model, a classification of the types of audio that the user identified as positive audio or negative audio. The method further includes identifying, with a microphone, audio in a physical environment. The method further includes splitting, with an audio machine-learning model, the audio into audio sources. The method further includes outputting, with the large language model, one or more matched audio sources that are matched with the one or more types of audio that the user categorized as positive audio or negative audio. The method further includes modifying output of an auditory device based on the one or more matched audio sources, wherein a positive audio source is amplified and a negative audio source is reduced or cancelled.