Acoustic Covariance Estimation via Spatial Source Separation
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Solution Overview
Problem
Existing beamformer designs face challenges in accurately estimating noise and interference covariance matrices, leading to imperfect signal cancellation and potential loss of optimality, especially when the signal of interest is continuously active, resulting in incorrect timing and side effects such as decreased signal-to-noise ratio.
Innovation Solution
A computing device is configured to receive acoustic data from a microphone array, transform it into the frequency domain, estimate covariance matrices, use acoustic imaging to determine spatial source distributions, and remove the signal of interest to generate noise and interference covariance matrices, thereby creating a beamformer that effectively suppresses noise and interference using minimum variance directional response techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gating mechanisms are used to estimate Sn when the source of interest is not active, then the covariance matrix estimation can exclude the signal of interest, but the gating is imperfect and can have incorrect timing even under moderate signal-to-noise ratio conditions
Solution Approach 1:
The patent extracts and removes the signal of interest from the covariance matrix estimation process by using spatial beamforming to separate the signal component from the noise component. Instead of relying on temporal gating to exclude the signal, the system spatially filters the signal out of the covariance calculation, allowing continuous operation without gating timing issues.
Solution Approach 2:
The patent introduces an intermediate spatial beamforming stage that acts as a mediator between the raw sensor data and the covariance matrix estimation. This beamforming stage preprocesses the data to separate signal and noise spatial components, providing a cleaner input for covariance estimation without requiring direct gating of the source signal.
2Productivity
If the source of interest is continuously active, then no gating mechanism exists to exclude the signal, but the sample covariance estimate of Sn includes the signal of interest causing the beamformer to treat it as noise and attempt to cancel it
Solution Approach 1:
The patent segments the total covariance matrix into distinct signal and noise components using spatial beamforming. By dividing the covariance estimation into separate signal-bearing and noise-only subspaces, the system can continuously operate while accurately estimating the noise covariance without contamination from the ongoing signal of interest.
3Reliability
If techniques are used to avoid signal cancellation effect, then the signal of interest is preserved, but there is loss of optimality of the designed beamformer
Solution Approach 1:
The patent performs preliminary spatial beamforming to separate the signal and noise components before covariance matrix estimation. This preliminary action creates a clean separation that allows the subsequent beamformer design to achieve optimality by working with accurate noise statistics, rather than having to compromise to preserve the signal.
Data Source
AI summary
A computing device is provided, comprising a processor configured to receive a set of measurements of a vector x of acoustic data, including noise, interference, and a signal of interest. The processor may express x in a frequency domain discretized in a plurality of intervals. For each interval, the processor may generate an estimate Ŝx of a covariance matrix of x. For each Ŝx, the processor may use acoustic imaging to obtain an estimate Ŷ of a spatial source distribution. For each Ŷ, the processor may remove the signal of interest to produce an estimate Ŵ of a noise and interference spatial source distribution. For each Ŵ, the processor may generate an estimate Ŝn of a noise and interference covariance matrix. The processor may generate a beamformer configured to remove noise and interference from the acoustic data, wherein the noise and interference at each frequency are identified using Ŝn.