Active Vibration Noise Control With Adaptive Voice Removal
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Solution Overview
Problem
Existing active noise controllers face challenges in effectively reducing noise while minimizing computational complexity, particularly when microphones detect occupant voices, leading to potential voice echoes and increased calculation demands.
Innovation Solution
An active vibration noise controller that includes a speaker for canceling noise, an error microphone, a reference microphone, and an adaptively updated removal filter to generate a corrected reference signal by removing voice components, using FIR filters to minimize computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a microphone is disposed in a headrest to detect noise, then noise detection capability is improved, but the microphone detects occupant voice components causing voice echoes and discomfort
Solution Approach 1:
The patent extracts and removes the voice component from the reference signal detected by the microphone. A removal filter is used to separate the voice component (which causes harmful echoes) from the noise signal, allowing the system to maintain noise detection capability while eliminating the harmful voice echo effect.
2Measurement precision
If voice component removal is performed using frequency band extraction and convolution calculation, then voice removal effectiveness is improved, but computational complexity and calculation time increase significantly
Solution Approach 1:
The patent employs a removal filter with limited memory capacity (short-living) that processes only the necessary portion of the signal. Instead of performing complex convolution calculations across the entire frequency band, the filter uses a finite impulse response with predetermined coefficients to efficiently remove voice components, significantly reducing computational load while maintaining effectiveness.
3Measurement precision
If the removal filter uses adaptive update based on current and previous reference signals, then voice component removal accuracy is improved, but processing time increases
Solution Approach 1:
The removal filter uses adaptive update that leverages previous reference signals to predict and remove voice components from current signals. By pre-establishing filter coefficients through adaptive learning from historical data, the system can quickly process new signals without requiring complex real-time calculations, thus improving accuracy while controlling processing time.
Data Source
AI summary
A noise controller comprises a speaker which outputs a canceling sound for canceling noise; an error microphone which generates an error signal from the noise and the canceling sound; and a reference microphone which detects reference signals. The noise controller comprises: a removal filter which is adaptively updated based on a current one of the reference signals and a previous one of the reference signal; and a filter processing unit which generates the canceling sound.