Dual-Microphone Adaptive Filtering for Body Sound Signal Noise Suppression
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Solution Overview
Problem
Remote auscultation systems face challenges in effectively suppressing environmental noise interference, leading to potential misdiagnosis due to the sensitivity of electret microphones and the complexity of noise filtering, especially with traditional normalized least mean square algorithms struggling to balance signal fidelity and convergence speed.
Innovation Solution
A dual-microphone adaptive filtering algorithm that applies high-pass filtering to both primary and secondary microphone signals, followed by a normalized least mean square algorithm to calculate adaptive filter weights, and then low-pass filtering to restore body sound signals, enhancing linear correlation and suppressing environmental noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If electret microphone pickup is used to collect body sound signals, then the structure design is simple and cost is low, but the microphone is very sensitive and collects environmental noise
Solution Approach 1:
The system is divided into two independent microphone channels: a primary microphone for capturing body sound signals and a secondary microphone for capturing environmental noise. This segmentation allows separate processing of signal and noise, enabling effective noise suppression while maintaining the simplicity and low cost of electret microphone usage.
2Productivity
If traditional normalized least mean square algorithm is used for adaptive filtering, then the filtering method can be applied, but the adjustment factor requires time-consuming and laborious adjustment to balance convergence speed and signal fidelity
Solution Approach 1:
The system employs an adaptive normalized least mean square algorithm that automatically adjusts the adjustment factor based on real-time signal characteristics. The algorithm self-regulates by computing the adjustment factor from the current signal energy and filter state, eliminating the need for manual parameter tuning while maintaining optimal convergence speed and signal fidelity.
3Manufacturing precision
If a small adjustment factor is selected to reduce signal distortion, then output distortion is reduced, but the filter weight converges too slowly to have practical application value
Solution Approach 1:
The adjustment factor is made dynamic rather than fixed. The adaptive algorithm continuously adjusts the adjustment factor based on the current signal conditions, filter convergence state, and signal energy levels. This dynamic adjustment allows the system to achieve both fast convergence and high signal fidelity by optimizing the parameter in real-time according to actual operating conditions.
4Measurement precision
If the amplitude of body sound signal is much larger than environmental noise, then the normalized least mean square algorithm causes mis-adjustment of adaptive filter parameters, but using a small adjustment factor slows convergence
Solution Approach 1:
The adaptive algorithm implements periodic monitoring and adjustment of the adjustment factor based on signal energy thresholds. When body sound signals with large amplitude are detected, the algorithm periodically adjusts the adjustment factor to prevent parameter mis-adjustment while maintaining acceptable convergence speed. This periodic adaptation allows the system to handle varying signal conditions effectively.
Data Source
AI summary
The present invention discloses a dual-microphone adaptive filtering algorithm for collecting body sound signals, characterized in that, using at least two microphones, a primary microphone and a secondary microphone, to collect signals; the primary microphone is used to collect noisy body sound signals, and the secondary microphone is used to collect environmental noise; applying a same high-pass filtering to signals collected by the primary microphone and signals collected by the secondary microphone; using a normalized least mean square algorithm on the primary microphone signals and the secondary microphone signals after the high-pass filtering to calculate a weight of the adaptive filter and to calculate an error signal to filter out environmental noise in the primary microphone signals; processing the error signal for a first time by a low-pass filtering to restore the body sound signals, to obtain the body sound signals output by the adaptive filtering algorithm. This algorithm not only may achieve rapid convergence of filter weights, but also avoid signal distortion, and suppress environmental noise interference quickly and reliably.