Hearing Aid Noise Classification for Speech Intelligibility
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Solution Overview
Problem
Hearing aids struggle to effectively reduce background noise and improve speech intelligibility, particularly in modulated noise environments, as existing methods do not adequately utilize available information for noise classification and adaptation.
Innovation Solution
A hearing aid system that classifies background noise by dividing the sound spectrum into frequency bands, estimating noise levels using the low percentile method, and retrieving signal processing parameters from a table to adjust gain levels and compression settings, optimizing speech intelligibility through noise environment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional noise reduction methods are used in hearing aids, then some background noise reduction is achieved, but speech intelligibility in modulated noise environments remains poor
Solution Approach 1:
The hearing aid implements dynamic noise classification that continuously adapts to changing noise environments. The system classifies noise into different types (e.g., modulated, unmodulated, speech-like) and dynamically adjusts signal processing parameters based on the current noise class, enabling effective speech enhancement in varying acoustic conditions
Solution Approach 2:
The system changes multiple signal processing parameters simultaneously based on noise classification results, including gain adjustments, frequency weighting, and processing algorithms. These parameter changes are tailored to the specific noise type detected, optimizing speech intelligibility for each noise scenario
2Reliability
If the hearing aid adapts signal processing to different noise environments, then speech intelligibility improves, but device complexity increases
Solution Approach 1:
The noise environment is segmented into distinct noise classes (e.g., modulated noise, unmodulated noise, speech-like noise). The signal processing system is also segmented into multiple processing paths, each optimized for specific noise types. This segmentation allows the complex adaptation task to be divided into manageable, specialized processing modules
Solution Approach 2:
The hearing aid performs automatic noise classification and self-adjusts its signal processing parameters without user intervention. The system autonomously monitors the acoustic environment, identifies noise types, and applies appropriate processing algorithms, eliminating the need for manual programming or user control
3Ease of operation
If the hearing aid uses automatic noise adaptation, then user comfort improves, but loss of time in accurate noise classification occurs
Solution Approach 1:
The system performs preliminary noise classification using quick acoustic features to rapidly identify the general noise type. Based on this preliminary classification, it then applies more sophisticated analysis only when needed, reducing the overall time required for accurate noise identification while maintaining classification accuracy
Data Source
AI summary
A hearing aid (30) includes a microphone (71), a signal processor (20) and an output transducer (22), and the signal processor (20) includes a set of audio processing parameters mapped to a set of stored noise classes (12) and a noise classification block (8) for classifying the background noise for the purpose of optimizing the frequency response in order to minimize the effects of the background noise. The hearing aid may further include a neural net for controlling the frequency response. A method for reducing a noise component in a signal includes the steps of classification of the noise component, comparing the noise component to a set of known noise components, and adapting the processed audio signals according to a corresponding set of frequency response parameters.