Wind Noise Detection via Spectral Distribution Differences
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Solution Overview
Problem
Existing wind noise detection methods in digital signal processing struggle to accurately differentiate wind noise from non-wind sounds due to phase differences and spectral level variations across microphones, leading to false detections, especially at higher frequencies and with increased microphone spacing.
Innovation Solution
A method and device that process digitized microphone signals by calculating the difference between the distributions of signal sample magnitudes from multiple microphones, ignoring phase differences and focusing on the unique impact of wind noise on signal distributions, with a detection threshold to indicate wind noise presence, allowing for selective wind noise reduction in specific frequency sub-bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microphone spacing is increased to improve wind noise detection capability, then wind noise detection sensitivity is improved, but false detection of non-wind sounds as wind noise increases
Solution Approach 1:
The patent divides the audio spectrum into multiple frequency sub-bands and processes each sub-band separately. This segmentation allows the system to apply different detection thresholds and methods to different frequency ranges, reducing false detections while maintaining wind noise detection sensitivity. The correlation calculation is performed independently for each sub-band, enabling targeted wind noise suppression without affecting the entire spectrum.
Solution Approach 2:
The patent changes the detection parameter from raw signal correlation to spectral correlation. By transforming the signals into the frequency domain and calculating correlation in the spectral domain, the system can distinguish wind noise from non-wind sounds more effectively. The spectral correlation coefficient is computed as a function of frequency, allowing the system to adapt to different operating conditions and microphone configurations.
2Measurement precision
If spectral correlation method is used to detect wind noise, then detection accuracy across frequency spectrum is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing computational resources only on frequency sub-bands where wind noise is actually present. Instead of processing the entire spectrum uniformly, the system identifies and processes only the relevant sub-bands, reducing overall computational complexity while maintaining detection accuracy. The correlation calculation is performed selectively based on the detected wind noise characteristics in each sub-band.
3Object-affected harmful factors
If wind noise suppression is applied across the entire audible spectrum, then wind noise reduction is maximized, but naturalness of audio signal deteriorates
Solution Approach 1:
The patent applies local quality by suppressing wind noise only in the specific frequency sub-bands where it is detected, rather than applying uniform suppression across the entire spectrum. The system calculates spectral correlation coefficients for each sub-band and applies suppression only where the correlation indicates wind noise presence. This localized approach preserves the natural characteristics of audio signals in sub-bands unaffected by wind noise.
Data Source
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AI summary
Processing digitized microphone signal data in order to detect wind noise. A first signal and a second signal are obtained from at least one microphone. The first and second signals reflect a common acoustic input, and are either temporally distinct or spatially distinct, or both. The first signal is processed to determine a first distribution of the samples of the first signal. The second signal is processed to determine a second distribution of the samples of the second signal. A difference between the first distribution and the second distribution is calculated. If the difference exceeds a detection threshold, an indication is output that wind noise is present.