Noise-Cancellation System Corrupted Sensor Signal Detection
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Solution Overview
Problem
Noise-cancellation systems face performance degradation due to corrupted sensor signals, which can occur from loose wiring connections or other causes, leading to 'crackling' sounds and affecting system performance during pre-production, production, and post-production stages.
Innovation Solution
A noise-cancellation system and method that utilize sensors to determine the power spectral density of sensor signals at various frequencies, calculate a correlation coefficient to assess linear association, and exclude signals exceeding a predetermined threshold to generate a noise-cancellation signal, thereby detecting and mitigating corrupted sensor inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor signals are used in noise-cancellation systems, then noise-cancellation performance is improved, but corrupted sensor signals cause performance degradation and crackling sounds
Solution Approach 1:
The system performs preliminary detection of corrupted sensor signals by analyzing power spectral density and calculating correlation coefficients before the corrupted signals can degrade noise-cancellation performance. This advance detection allows the system to identify and exclude faulty signals proactively, preventing crackling sounds and performance degradation.
Solution Approach 2:
The system extracts and removes corrupted sensor signals from the noise-cancellation processing chain by comparing correlation coefficients against thresholds. By separating and excluding the harmful corrupted signals while retaining clean sensor inputs, the system maintains reliable noise-cancellation performance without interference from faulty sensors.
2Reliability
If sensor signal corruption is detected and excluded, then noise-cancellation performance is maintained, but system complexity increases due to additional processing steps
Solution Approach 1:
The system replaces complex physical sensor filtering mechanisms with computational signal processing. By using mathematical operations (power spectral density analysis, correlation coefficient calculation) to detect and exclude corrupted signals, the system achieves reliable noise-cancellation performance through software-based solutions rather than additional hardware complexity.
Solution Approach 2:
The system monitors changes in signal parameters (power spectral density distribution, correlation coefficients) to detect corruption. By establishing threshold criteria for these parameters, the system can automatically identify and exclude corrupted signals using simple comparative operations, maintaining performance consistency without requiring complex processing algorithms.
3Measurement precision
If power spectral density analysis is performed at multiple frequencies, then corrupted signals are detected more accurately, but computational time and processing load increase
Solution Approach 1:
The system performs power spectral density analysis at multiple frequency points to ensure accurate detection of corrupted signals, accepting the additional computational time as necessary to maintain high detection precision. This partial action approach analyzes only the essential frequency components needed to identify corruption patterns without performing exhaustive full-spectrum analysis.
Solution Approach 2:
The system performs preliminary power spectral density analysis to quickly identify obvious corruption patterns before applying more computationally intensive correlation coefficient calculations only when needed. This staged approach reduces overall processing time while maintaining high detection accuracy by applying full analysis only to suspicious signals.
Data Source
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AI summary
A noise-cancellation system, including: a plurality of sensors, each sensor outputting a sensor signal; a controller configured to receive each sensor signal, and, for each sensor signal, to: determine a power of the sensor signal at a plurality of frequencies; determine a measure of association between the power of the sensor signal at the plurality of frequencies and frequency; and determine whether the measure of association exceeds a predetermined threshold, wherein the processor is further configured to compute a noise-cancellation signal using the sensor signals, wherein the noise-cancellation signal is computed excluding sensor signals that were determined to exceed the predetermined threshold; and at least one actuator receiving the noise-cancellation signal and producing a noise-cancellation audio signal.