Hearing Aid Speech Restoration with Dominant-Speech Synthesis
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Solution Overview
Problem
Conventional hearing aid technologies face challenges in noise uncertainty and speech distortion due to diverse noise sources, leading to ineffective noise reduction and amplification of frequencies that are not user-specific, especially in noisy environments.
Innovation Solution
A system utilizing microphone arrays, beamforming algorithms, and AI-driven speech recognition to identify dominant speech or music, convert it into intermediate linguistic representations, and synthesize high-quality speech tailored to individual hearing profiles, enhancing speech clarity and reducing noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hearing aids amplify sound volumes and apply noise reduction algorithms, then speech clarity is improved, but speech distortion and noise uncertainty increase due to diverse noise sources
Solution Approach 1:
The patent extracts and isolates the dominant speech signal from the noisy mixture by converting to frequency domain representation and applying spectral subtraction. Instead of trying to filter all noises, the system extracts only the relevant speech components while removing background noises, thereby improving speech clarity without introducing distortion.
Solution Approach 2:
The patent changes the representation parameters of the audio signal by transforming from time domain to frequency domain using Short-Time Fourier Transform. This parameter transformation enables effective noise reduction by allowing selective manipulation of different frequency components, improving speech clarity while maintaining reliability across diverse noise conditions.
2Reliability
If directional microphones and beamforming algorithms are used to focus on target speech, then noise reduction is improved, but speech distortion increases due to sensitivity changes around the focused angle
Solution Approach 1:
The patent segments the audio signal into multiple frequency bins using Fourier transform, allowing independent processing of each frequency component. This segmentation enables the system to apply noise reduction selectively to different frequency ranges while preserving speech components, thereby reducing noise without causing speech distortion.
Solution Approach 2:
The patent introduces an intermediary spectral representation as a mediator between the raw noisy signal and the final output. By working in the frequency domain and using spectral subtraction as an intermediary processing step, the system can effectively separate speech from noise without the directional sensitivity issues that plague beamforming approaches.
3Measurement precision
If hearing aids apply frequency-specific amplification based on settings, then certain frequencies are enhanced, but user-specific hearing needs are not met due to generalization
Solution Approach 1:
The patent enables the hearing aid system to automatically adapt to each user's specific hearing needs by analyzing their individual auditory response characteristics. The system performs self-adjustment by measuring the user's hearing thresholds and automatically optimizing frequency-specific gain settings, eliminating the need for manual programming and ensuring personalized hearing optimization.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors the user's hearing response and adjusts amplification settings accordingly. By measuring the output signal and comparing it with the desired target, the system automatically fine-tunes frequency-specific gains to match the user's specific hearing profile, improving both accuracy and adaptability.
Data Source
AI summary
This invention provides a new and improved hearing aid system with high quality noise cancellation method and devices to overcome the limitations and difficulties encountered in conventional technologies. The technical limitations of the noise uncertainty and speech distortion in the hearing aid field are resolved by restoration of the high-quality speech by converting the speech content into an intermediate linguistic representation and by synthesizing the speech of the same speaker with pre-trained using artificial intelligence (AI) modules. In this invention, the noise uncertainties are circumvented by focusing on the target speaker or picking up the dominant speech by choosing the corresponding setting assuming the speech from the target speaker is the dominant speech based on the Lombard effect.