Adaptive ANC Tuning for Semi-In-Ear Seal Loss
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Solution Overview
Problem
Existing active noise cancellation (ANC) technologies face challenges in achieving ideal noise reduction effects due to poor ear seal and varying wearing tightness of semi-in-ear headphones, requiring high processing power and storage resources, and struggling to accurately match complex usage scenarios.
Innovation Solution
A self-adaptive adjustment method for ANC parameters that involves determining energy loss thresholds and frequency points, calculating a main frequency, and adjusting the chip to a target mode to optimize ANC parameter settings in real-time, using a combination of test audio and feedback microphones to dynamically adjust noise reduction curves and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed ANC parameters are used, then device complexity is reduced, but noise reduction effect deteriorates due to poor ear seal and varying wearing tightness
Solution Approach 1:
The patent implements dynamic ANC parameters that automatically adapt to different wearing scenarios. The system detects ear seal quality and wearing tightness in real-time, then dynamically adjusts ANC parameters (such as filter coefficients and gain values) to maintain optimal noise reduction performance across varying conditions, transforming fixed parameters into adaptive dynamic parameters.
Solution Approach 2:
The patent changes ANC parameters based on detected wearing conditions. When poor ear seal or loose fitting is detected, the system modifies parameters such as frequency response curves, filter orders, and cancellation gains to compensate for the degraded seal, thereby maintaining reliable noise reduction without requiring complex hardware changes.
2Reliability
If all filter parameters are recalculated according to wearing situation, then noise reduction effect is improved, but power consumption increases due to high processing power requirements
Solution Approach 1:
Instead of recalculating all filter parameters comprehensively, the patent performs partial updates by adjusting only the critical ANC parameters that have the most significant impact on noise reduction performance. This selective parameter adjustment maintains effective noise cancellation while substantially reducing the computational load and power consumption compared to full parameter recalculation.
Solution Approach 2:
The patent pre-calculates and stores multiple sets of ANC parameters corresponding to different wearing scenarios (e.g., good seal, poor seal, tight fit, loose fit). During operation, the system quickly selects and switches between pre-prepared parameter sets based on real-time detection, avoiding the need for complex real-time calculations and reducing processing power requirements.
3Adaptability or versatility
If multiple preset noise reduction curves are used, then adaptability to different scenarios is improved, but storage resources are consumed and real-time matching of complex scenarios is difficult
Solution Approach 1:
The patent replaces static preset curves with dynamic parameter generation. Instead of storing multiple fixed noise reduction curves, the system generates appropriate ANC parameters in real-time based on detected wearing conditions, enabling continuous adaptation to complex scenarios without being limited by pre-defined curve options and reducing storage requirements.
Solution Approach 2:
The system performs self-adjustment by automatically detecting wearing conditions and generating appropriate ANC parameters without requiring external input or extensive preset libraries. The detection algorithm and parameter generation work together in a self-service manner, enabling the device to adapt to complex scenarios autonomously with minimal storage resources.
Data Source
AI summary
Disclosed are a self-adaptive adjustment method of active noise cancellation (ANC) parameter, a device and a computer-readable storage medium. The method includes: obtaining frequency points of noise reduction headphone with energy loss and energy loss values of each frequency point, and determining a quantity of the frequency points with the energy loss values are greater than a first energy loss threshold; determining whether a sum of the energy loss values is greater than a second energy loss threshold, and determining whether the quantity of frequency points is greater than a preset frequency point quantity threshold.