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Extending Nonnegative Matrix Factorization—A discussion in the context of multiple frequency estimation of musical signals

机译:扩展非负矩阵分解-在音乐信号多频估计的背景下进行的讨论

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Nonnegative Matrix Approximation (NNMA) is a very well known technique of multivariate data analysis. However, in its basic form it provides very little control over its behaviour. This article explores possible extensions to this method in the context of multiple frequency estimation: using a parameterized distortion measure, enforcing harmonic structure on the basis matrix and introducing additional regularizations. We provide the reader with three regularizations useful for multiple pitch estimation and propose an objective way of evaluating the performance of NNMA-based pitch estimators. Finally, we use this evaluation method to train the parameters of our regularized harmonic NNMA and present the results.
机译:非负矩阵逼近(NNMA)是一种非常著名的多元数据分析技术。但是,以其基本形式,它几乎无法控制其行为。本文探讨了在多频率估计的情况下对该方法的可能扩展:使用参数化失真度量,在基础矩阵上增强谐波结构并引入其他正则化。我们为读者提供了三个可用于多重基音估计的正则化方法,并提出了一种客观的方法来评估基于NNMA的基音估计器的性能。最后,我们使用这种评估方法来训练正则谐波NNMA的参数并给出结果。

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