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Blind Source Separation in the Time-Frequency Domain Based on Multiple Hypothesis Testing

机译:基于多重假设检验的时频域盲源分离

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This paper considers a time-frequency ( ${t}$-${f}$ )-based approach for blind separation of nonstationary signals. In particular, we propose a time-frequency “point selection” algorithm based on multiple hypothesis testing, which allows automatic selection of auto- or cross-source locations in the time-frequency plane. The selected ${t}$- ${f}$ points are then used via a joint diagonalization and off-diagonalization algorithm to perform source separation. The proposed algorithm is developed assuming deterministic signals with additive white complex Gaussian noise. A performance comparison of the proposed and existing approaches is provided.
机译:本文考虑了一种基于时频($ {t} $-$ {f} $)的非平稳信号盲分离方法。特别是,我们提出了一种基于多重假设检验的时频“点选择”算法,该算法允许自动选择时频平面中自动或交叉源位置。然后,通过对角化和非对角化联合算法,使用选定的$ {t} $-$ {f} $点执行源分离。在假定信号具有加性白色复高斯噪声的情况下,开发了该算法。提供了建议的方法和现有方法的性能比较。

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