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On application of non-negative matrix factorization for ad hoc microphone array calibration from incomplete noisy distances

机译:基于非负矩阵分解的不完全噪声距离在自组织麦克风阵列校准中的应用

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We propose to use non-negative matrix factorization (NMF) to estimate the unknown pairwise distances and reconstruct a distance matrix for microphone array position calibration. We develop new multiplicative update rules for NMF with incomplete input matrix that take into account the symmetry of the distance matrix. Additionally, we develop a convex matrix completion method which is related to an l-regularized symmetric NMF. Thorough experiments demonstrate that the proposed methods lead to substantial improvement over the state-of-the-art techniques in a wide range of signal-to-noise and unknown-distance ratios. The convex symmetric matrix completion method was found to be the most robust method with less computational cost.
机译:我们建议使用非负矩阵分解(NMF)来估计未知的成对距离,并重建用于麦克风阵列位置校准的距离矩阵。我们使用不完整的输入矩阵开发NMF的新乘法更新规则,以考虑距离矩阵的对称性。另外,我们开发了与L-正规化对称NMF相关的凸矩阵完成方法。彻底的实验表明,所提出的方法导致在广泛的信号对噪声和未知距离比中的最先进技术上的显着提高。发现凸对称矩阵完成方法是具有较少计算成本的最强大的方法。

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