Adaptive Beamforming for Variable Microphone Orientation
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Solution Overview
Problem
Existing dual microphone algorithms in headsets assume a fixed microphone array orientation relative to the user's mouth, which is not feasible in dynamic environments like in-ear fitness headsets, leading to poor noise reduction and voice quality issues due to varying microphone positions and background noise.
Innovation Solution
A method and integrated circuit for a dual microphone array that dynamically adjusts thresholds and gain functions based on speech and noise direction ranges, background noise levels, and noise types to reduce noise while preserving desired speech, using spatial statistics and adaptive beamforming techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a dual microphone array is used with fixed orientation assumption, then noise reduction performance is improved, but voice quality deteriorates in dynamic environments where microphone orientation varies
Solution Approach 1:
The patent applies dynamics by making the beamforming system adaptive rather than fixed. The system dynamically adjusts beamformer parameters including look direction, speech acceptance angle, and noise rejection angle based on real-time estimation of speech and noise directions. This allows the noise reduction system to maintain effectiveness while adapting to varying microphone orientations relative to the user's mouth during physical activity.
Solution Approach 2:
The patent changes key parameters of the beamforming algorithm based on detected conditions. Specifically, it modifies the look direction, speech acceptance angle, and noise rejection angle parameters according to the estimated speech direction and noise direction. This parameter adaptation enables the system to maintain optimal noise reduction performance across different physical positions and orientations.
2Volume of moving object
If microphones are placed close to the receiver in the earbud, then device size is reduced, but echo-related problems increase
Solution Approach 1:
The patent moves the microphone array from the earbud body to the ear hook, utilizing the spatial dimension provided by the curved ear hook structure. This dimensional relocation allows microphones to be positioned farther from the receiver while still maintaining a compact overall headset design, thereby reducing echo-related problems caused by microphone-receiver proximity.
3Device complexity
If a single microphone-based noise reduction algorithm is used, then device complexity is reduced, but voice quality deteriorates in noisy environments
Solution Approach 1:
The patent implements a dynamic adaptive beamforming system that adjusts its parameters in real-time based on the acoustic environment. The system continuously estimates speech and noise directions and modifies beamformer parameters accordingly, enabling effective noise reduction in noisy environments while maintaining manageable device complexity through software-based adaptability.
4Object-affected harmful factors
If beamformers are used for voice processing, then noise reduction is improved, but desired speech may be suppressed when speech direction is not constant
Solution Approach 1:
The patent dynamically changes the beamformer parameters including the look direction, speech acceptance angle, and noise rejection angle based on real-time estimation of speech and noise directions. This parameter adaptation ensures that the desired speech is consistently captured within the acceptance angle while noise from other directions is rejected, maintaining speech preservation reliability even when speech direction varies.
Solution Approach 2:
The system employs feedback by continuously monitoring the acoustic environment, estimating speech and noise directions, and using this information to adjust beamformer parameters. This closed-loop approach ensures that the beamforming system adapts to changing conditions while maintaining reliable speech capture and noise rejection.
Data Source
AI summary
A method may include determining a desired speech estimate originating from a speech acceptance direction range while reducing a level of interfering noise, determining an interfering noise estimate originating from a noise rejection direction range while reducing a level of desired speech, calculating a ratio of the desired speech estimate to the interfering noise estimate, dynamically computing a set of thresholds based on the speech acceptance direction range, noise rejection direction range, a background noise level, and a noise type, estimating a power spectral density of background noise arriving from the noise rejection direction range, calculating a frequency-dependent gain function based on the power spectral density of background noise and thresholds, and applying the frequency-dependent gain function to at least one microphone signal generated by the plurality of microphones to reduce noise arriving from the noise rejection direction while preserving desired speech arriving from the speech acceptance direction.