松弛双耳波束形成优化问题的凸逼近
A Convex Approximation of the Relaxed Binaural Beamforming Optimization Problem
摘要：
最近提出的放松双耳波束成形（RBB）优化问题提供了一个灵活的权衡噪声抑制和双耳线索保存的声源在声学场景。它最小化输出噪声功率，在保证目标在处理后保持不变并且声源的双耳线索失真将小于用户定义的阈值的约束下。然而，RBB问题是一个计算要求很高的非凸优化问题。近似求解RBB的唯一现有次优方法是连续凸优化（SCO）方法，其通常需要求解每个频率仓的多个凸优化问题，以便收敛。当RBB优化问题的所有约束都满足时，收敛性就达到了。在本文中，我们提出了一个半定凸松弛（SDCR）的RBB优化问题。所提出的次优SDCR方法解决了每个频率点的单个凸优化问题，从而导致比SCO方法低得多的计算复杂性。与SCO方法不同，SDCR方法不保证用户控制的上限双耳线索失真。为了解决这个问题，我们还提出了一个次优的混合方法，结合SDCR和SCO方法。与听力测试相结合的仪器措施表明，SDCR和混合方法实现显着较低的计算复杂性比SCO方法，在大多数情况下，更好的权衡预测的可懂度和双耳提示保存比SCO方法。
英文摘要：
The recently proposed relaxed binaural beamforming (RBB) optimization problem provides a flexible trade-off between noise suppression and binaural-cue preservation of the sound sources in the acoustic scene. It minimizes the output noise power, under the constraints which guarantee that the target remains unchanged after processing and the binaural-cue distortions of the acoustic sources will be less than a user-defined threshold. However, the RBB problem is a computationally demanding non-convex optimization problem. The only existing suboptimal method which approximately solves the RBB is a successive convex optimization (SCO) method which, typically, requires to solve multiple convex optimization problems per frequency bin, in order to converge. Convergence is achieved when all constraints of the RBB optimization problem are satisfied. In this paper, we propose a semi-definite convex relaxation (SDCR) of the RBB optimization problem. The proposed suboptimal SDCR method solves a single convex optimization problem per frequency bin, resulting in a much lower computational complexity than the SCO method. Unlike the SCO method, the SDCR method does not guarantee user-controlled upper-bounded binaural-cue distortions. To tackle this problem we also propose a suboptimal hybrid method which combines the SDCR and SCO methods. Instrumental measures combined with a listening test show that the SDCR and hybrid methods achieve significantly lower computational complexity than the SCO method, and in most cases better trade-off between predicted intelligibility and binaural-cue preservation than the SCO method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
