用最优传输均值联合估计麦克风阵列的混响响应
Room Impulse Response Estimation through Optimal Mass Transport Barycenters
- 通过最优传输均值共享麦克风间的延迟结构信息
- 在短信号低信噪比下仍能稳定估计混响响应
- 适合语音增强与声学还原等实际场景
本文研究紧密排列麦克风阵列上一组房间混响响应（RIR）的联合估计问题。准确估计RIR对语音增强、降噪和声学还原等应用至关重要，但实际中常因激励信号短、信噪比低、频谱激发不足导致问题病态。为此，本文提出基于最优质量传输（OMT）正则化的解决方案，利用OMT质心（即广义均值）作为麦克风间信息共享机制，无需假设房间声学特性即可量化并利用各麦克风间延迟结构的相似性。所提估计器可表述为凸优化问题，可用标准求解器实现。数值实验表明，该方法在原本病态的估计场景下仍具显著性能提升。
原文摘要 · Abstract (English)
In this work, we consider the problem of jointly estimating a set of room impulse responses (RIRs) corresponding to closely spaced microphones. The accurate estimation of RIRs is crucial in acoustic applications such as speech enhancement, noise cancellation, and auralization. However, real-world constraints such as short excitation signals, low signal-to-noise ratios, and poor spectral excitation, often render the estimation problem ill-posed. In this paper, we address these challenges by means of optimal mass transport (OMT) regularization. In particular, we propose to use an OMT barycenter, or generalized mean, as a mechanism for information sharing between the microphones. This allows us to quantify and exploit similarities in the delay-structures between the different microphones without having to impose rigid assumptions on the room acoustics. The resulting estimator is formulated in terms of the solution to a convex optimization problem which can be implemented using standard solvers. In numerical examples, we demonstrate the potential of the proposed method in addressing otherwise ill-conditioned estimation scenarios.
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