Adaptive Noise Cancellation Using Dynamic Coefficient Adjustment
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Solution Overview
Problem
Existing noise cancellation systems in audio devices face challenges in adapting to variations in microphone sensitivity, positioning, and environmental changes, leading to reduced robustness in noise reduction.
Innovation Solution
The method involves determining pitch salience and energy estimates from audio signals received by multiple microphones, adapting coefficients to generate modified signals that effectively suppress noise, and using transfer functions to differentiate between speech and noise components, thereby enhancing adaptivity in noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If noise cancellation uses fixed coefficients, then processing is simple, but adaptability to variations in microphone sensitivity and positioning is poor
Solution Approach 1:
The patent implements dynamic coefficient adaptation where the noise cancellation coefficients are continuously adjusted based on real-time analysis of audio signals. The system monitors pitch salience and energy estimates to dynamically modify coefficients, allowing the system to adapt to varying microphone sensitivity and positioning conditions without requiring manual intervention or complex reconfiguration.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors the output of noise cancellation processing and adjusts coefficients based on feedback signals. By analyzing pitch salience and energy estimates from the processed audio signals, the system automatically optimizes coefficients to maintain effective noise cancellation under varying conditions, resolving the contradiction between adaptability and complexity through intelligent control.
2Reliability
If the system adapts coefficients continuously, then noise cancellation robustness improves, but processing time increases
Solution Approach 1:
The patent implements periodic coefficient adaptation where the system adjusts coefficients at specific intervals or when certain conditions are met, rather than continuously. The system monitors pitch salience and energy estimates and triggers coefficient updates only when necessary, such as when detecting changes in acoustic environment or microphone conditions. This periodic approach maintains noise cancellation robustness while minimizing processing time and computational overhead.
Solution Approach 2:
The system performs self-adjustment by automatically monitoring its own performance through pitch salience and energy estimate analysis. The noise cancellation system evaluates its own output and autonomously modifies coefficients without external intervention, enabling it to maintain robustness under varying conditions while avoiding unnecessary processing steps that would increase time consumption.
3Measurement precision
If pitch salience threshold is set high, then speech component is preserved, but noise suppression effectiveness decreases
Solution Approach 1:
The patent dynamically adjusts the pitch salience threshold parameter based on current acoustic conditions and energy estimates. Rather than using a fixed threshold, the system modifies the threshold value in response to varying noise levels, speech characteristics, and environmental conditions. This allows the system to optimize the balance between preserving speech components and suppressing noise, resolving the contradiction by making the threshold adaptive rather than static.
Solution Approach 2:
The patent applies different pitch salience thresholds for different frequency bands and temporal segments of the audio signal. By analyzing local characteristics of the signal at different positions and frequencies, the system can use higher thresholds in certain regions to preserve speech while using lower thresholds in other regions to maximize noise suppression. This localized approach allows simultaneous optimization of both speech preservation and noise suppression effectiveness.
Data Source
AI summary
Systems and methods for controlling adaptivity of noise cancellation are presented. One or more audio signals are received by one or more corresponding microphones. The one or more signals may be decomposed into frequency sub-bands. Noise cancellation consistent with identified adaptation constraints is performed on the one or more audio signals. The one or more audio signals may then be reconstructed from the frequency sub-bands and outputted via an output device.