Spherical Microphone Array Noise Reduction via Adaptive Wiener Filtering
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Solution Overview
Problem
Spherical microphone arrays on a rigid sphere face challenges in minimizing noise, particularly low-frequency noise, in Ambisonics representations due to the amplification of noise over higher order coefficients, which distorts the sound field representation.
Innovation Solution
A method is introduced to compute an optimization parameter for Tikhonov regularization using the signal-to-noise ratio of microphone capsule signals, employing time-variant Wiener filters to adapt the transfer function and reduce noise, thereby enhancing the Ambisonics representation's accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the inverse filter response is applied to remove the impact of the microphone array, then the Ambisonics representation accuracy is improved, but the transducer noise is amplified especially at low frequencies
Solution Approach 1:
The patent applies Tikhonov regularization to modify the inverse filter response by introducing a regularization parameter that controls the trade-off between noise amplification and sound field reconstruction accuracy. This parameter adjustment suppresses the amplification of transducer noise while preserving the essential directional information in the Ambisonics representation.
Solution Approach 2:
The patent implements time-variant Wiener filters that adapt the regularization parameter dynamically based on the signal-to-noise ratio estimated from the microphone signals. This allows the filter to optimize its behavior in real-time, reducing noise amplification when noise is dominant while maintaining high accuracy when the signal is strong.
2Object-generated harmful factors
If Tikhonov regularization is applied to reduce noise amplification, then the harmful noise effect is reduced, but a regularization parameter must be manually adapted by trial and error
Solution Approach 1:
The patent introduces an automatic feedback mechanism that estimates the signal-to-noise ratio from the microphone signals and uses this estimation to automatically adjust the regularization parameter. This eliminates the need for manual trial-and-error adaptation while optimizing the noise reduction performance according to the actual recording conditions.
Solution Approach 2:
The system performs self-adjustment of the regularization parameter by analyzing its own input signals and automatically optimizing the filtering parameters based on the estimated noise characteristics, making the system self-adapting without external intervention.
3Object-generated harmful factors
If high orders are faded out for low frequencies to reduce noise, then the noise amplification is reduced, but the spatial resolution for low frequencies is decreased
Solution Approach 1:
Instead of uniformly fading out high orders, the patent uses Tikhonov regularization to selectively suppress only those frequency components and spatial orders where noise dominates. This preserves the spatial resolution for low frequencies where the signal-to-noise ratio is sufficient, while still reducing noise amplification where appropriate.
Data Source
AI summary
Spherical microphone arrays capture a three-dimensional sound field (P(Ωct)) for generating an Ambisonics representation (Anm(t)), where the pressure distribution on the surface of the sphere is sampled by the capsules of the array. The impact of the microphones on the captured sound field is removed using the inverse microphone transfer function. The equalization of the transfer function of the microphone array is a big problem because the reciprocal of the transfer function causes high gains for small values in the transfer function and these small values are affected by transducer noise. The present principles minimize that noise by using a Wiener filter processing (34) in the frequency domain, which processing is automatically controlled (33) per wave number by the signal-to-noise ratio of the microphone array.