Hearing Aid Audio Source Separation via Auxiliary Processing
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Solution Overview
Problem
Current hearing aid systems struggle to effectively distinguish and enhance specific individual sound sources in complex environments, such as multiple conversations, due to limitations in processing power and filtering capabilities.
Innovation Solution
A hearing aid system that leverages an auxiliary processing device to separate and enhance audio sources by identifying individual sound sources, determining their relevance, and applying filter updates wirelessly to earpieces, utilizing machine-learning algorithms and remote computing for improved processing power and low-latency audio delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hearing aid systems use traditional filtering capabilities to distinguish sound sources, then device complexity is reduced, but audio quality and source separation accuracy deteriorate in complex environments
Solution Approach 1:
The patent introduces an auxiliary processing device as an intermediary between the hearing aid system and the complex audio processing tasks. This external device performs machine learning-based source separation and sends filter updates back to the hearing aid, allowing high-precision processing without increasing the complexity of the hearing aid itself.
Solution Approach 2:
The patent replaces traditional mechanical/electronic filtering mechanisms with machine learning algorithms that run on an auxiliary processing device. This substitution enables superior source separation accuracy by using computational intelligence rather than conventional signal processing techniques.
2Measurement precision
If hearing aid systems increase processing power to handle complex audio environments, then audio quality improves, but battery drain increases
Solution Approach 1:
The patent segments the processing tasks between the hearing aid device and an auxiliary processing device. The auxiliary device handles the computationally intensive machine learning operations, while the hearing aid performs lighter tasks such as audio capture and playback. This segmentation reduces battery drain on the hearing aid while maintaining high audio quality.
Solution Approach 2:
The auxiliary processing device acts as an intermediary that offloads energy-intensive computations from the hearing aid's battery. By performing source separation and filter generation externally, the system achieves high audio quality without proportionally increasing the hearing aid's power consumption.
3Measurement precision
If hearing aid systems implement sophisticated source separation algorithms, then intelligibility improves, but processing time increases
Solution Approach 1:
The system performs preliminary source separation and filter generation on recorded audio data before the user needs to hear it. The auxiliary processing device analyzes audio segments, identifies sources, and prepares filter updates in advance, reducing the processing delay during real-time listening while maintaining high intelligibility.
Data Source
AI summary
Audio enhancement systems, devices, methods, and computer program products are disclosed. In particular embodiments, audio is separated by source at an auxiliary processing device, primary voice presence and/or relevancy per source is determined and used to determine enhancement data that is sent to one or more earpieces and used for enhancing audio at the earpiece. These and other embodiments are disclosed herein.