Low Complexity Beamformer for Hearing Devices
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Solution Overview
Problem
Existing hearing assistive devices face challenges in efficiently enhancing speech, particularly in removing background noise, due to variations in acoustical differences across users and microphone sensitivity, which affect the performance of time-invariant beamformers.
Innovation Solution
A multi-microphone enhancement system operating in the time-frequency domain, comprising a robust time-invariant beamformer, noise field adaptation, and target steering adaptation, to improve noise reduction performance while maintaining low complexity and memory consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a time-invariant beamformer is used with fixed steering vector, then device complexity is reduced, but noise reduction performance deteriorates due to acoustical differences across users and microphone sensitivity variations
Solution Approach 1:
The patent introduces adaptive mechanisms that allow the beamformer to dynamically adjust to user-specific acoustic conditions. The system adapts the steering vector based on individual user characteristics and noise field conditions, transforming the static time-invariant beamformer into a dynamic system that maintains low computational complexity while improving noise reduction performance for each user.
Solution Approach 2:
The patent changes the parameters of the beamformer by introducing user-specific acoustic transfer functions and adaptive steering vectors. Instead of using fixed parameters, the system adjusts the steering vector parameters based on individual user measurements and noise field characteristics, thereby improving performance without significantly increasing device complexity.
2Reliability
If a calibrated beamformer is used for own voice enhancement, then speech enhancement performance is improved, but adaptability to different users and noise fields deteriorates
Solution Approach 1:
The patent performs preliminary calibration measurements for each user to capture their specific acoustic characteristics. These measurements are stored and used to initialize the adaptive beamformer, allowing the system to start with user-specific parameters rather than generic defaults. This preliminary action enables both high performance and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor the acoustic environment and user-specific characteristics. The beamformer uses this feedback to adaptively adjust its parameters in real-time, maintaining optimal performance across different users and noise fields. The feedback loop enables the system to learn and adapt to individual user patterns.
Data Source
AI summary
A hearing device includes a) a multitude of input transducers providing a corresponding multitude of electric input signals; and b) a processor for providing a processed signal in dependence of the electric input signals. The processor includes b1) a beamformer for providing a spatially filtered signal in dependence of electric input signals and beamformer filter coefficients determined in dependence of a fixed steering vector including as elements respective acoustic transfer functions from a target signal source, to each of said multitude of input transducers; and b2) a target adaptation module connected to the input transducers and to at least one beamformer, the target adaptation module being configured to provide compensation signals to compensate the electric input signals so that they match the fixed steering vector.