Calibration-Based Beamforming for Noise-Resistant Headsets
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Solution Overview
Problem
Headsets with small form factors face challenges in audio performance due to the microphone's increased susceptibility to environmental noise, necessitating a tradeoff between audio quality and usability features like comfort and portability.
Innovation Solution
A noise-reducing system that uses an array of microphones and a reference microphone to calibrate filter parameters, forming a beamformer output signal, which is then filtered using a non-linear adaptive filter adapted based on non-speech signal portions detected by a speech detection sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If the microphone is placed farther from the user's mouth to achieve small form factor, then comfort and portability are improved, but susceptibility to environmental noise increases
Solution Approach 1:
The system divides the microphone array into multiple individual microphones positioned at different locations, allowing independent signal processing for each microphone. This segmentation enables the system to compensate for the increased distance from the mouth by using multiple spatial samples to reconstruct the speech signal while rejecting noise.
Solution Approach 2:
A reference microphone is introduced as an intermediary element that captures a copy of the speech signal from a different spatial position. This reference signal serves as a mediator to calibrate the beamformer and adaptive filter, allowing the system to distinguish between the desired speech signal and environmental noise by comparing signals from multiple microphones.
2Measurement precision
If a beamformer is used to process microphone signals, then speech signal enhancement is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary calibration of the beamformer filter parameters using the reference microphone signal before actual speech processing begins. This preliminary action allows the complex beamforming computations to be optimized in advance, reducing the computational burden during real-time speech enhancement while maintaining high signal-to-noise ratio improvement.
Solution Approach 2:
The adaptive filter parameters are dynamically adjusted based on the calibration signal and speech detection results. The system transitions from a static beamformer to a dynamic system that adapts its filtering characteristics in real-time, allowing it to maintain optimal performance while managing computational resources more efficiently.
3Object-affected harmful factors
If non-linear adaptive filtering is applied to the beamformer output, then noise reduction is improved, but processing time increases
Solution Approach 1:
The non-linear adaptive filter operates continuously on the beamformer output signal, maintaining noise reduction performance throughout the entire audio stream. By keeping the filter active and adapting continuously rather than applying processing in discrete batches, the system achieves effective noise minimization without significant interruptions or delays in the audio processing pipeline.
Data Source
AI summary
A first set of signals from an array of one or more microphones, and a second signal from a reference microphone are used to calibrate a set of filter parameters such that the filter parameters minimize a difference between the second signal and a beamformer output signal that is based on the first set of signals. Once calibrated, the filter parameters are used to form a beamformer output signal that is filtered using a non-linear adaptive filter that is adapted based on portions of a signal that do not contain speech, as determined by a speech detection sensor.