论文标题
正弦与近线性计算复杂性的混合物的盲源分离
Blind Source Separation for Mixture of Sinusoids with Near-Linear Computational Complexity
论文作者
论文摘要
我们提出了一种多色调分解算法，可以在嘈杂的观察序列中找到基本正弦的频率，幅度和相。在独立分布的高斯噪声下，我们的方法利用一种最大似然方法来估计受污染观测值的相关音调参数。当估计$ M $的正弦源数量时，我们的算法依次估算其频率并共同优化其振幅和相位。在没有有关$ M $的信息的情况下，我们的方法也可以作为盲源分离器实现。我们算法的计算复杂性是接近线性的，即$ \ tilde {o}（n）$。
We propose a multi-tone decomposition algorithm that can find the frequencies, amplitudes and phases of the fundamental sinusoids in a noisy observation sequence. Under independent identically distributed Gaussian noise, our method utilizes a maximum likelihood approach to estimate the relevant tone parameters from the contaminated observations. When estimating $M$ number of sinusoidal sources, our algorithm successively estimates their frequencies and jointly optimizes their amplitudes and phases. Our method can also be implemented as a blind source separator in the absence of the information about $M$. The computational complexity of our algorithm is near-linear, i.e., $\tilde{O}(N)$.
