Non-negative matrix factorisation incorporating greedy hellinger sparse coding applied to polyphonic music transcription
Non-negative matrix factorisation incorporating greedy hellinger sparse coding applied to polyphonic music transcription
复制标题
结合贪婪 Hellinger 稀疏编码的非负矩阵分解应用于复调音乐转录
DOI：
10.1109/icassp.2015.7178364
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Mark D. Plumbley
中科院分区：
文献类型：
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作者：
K. O'Hanlon;M. Sandler;Mark D. Plumbley
非负矩阵分解（NMF）是许多音乐信号处理任务中常用的工具，包括自动音乐转录（AMT）。然而，无监督的NMF在这种情况下被认为是有问题的，并且已经提出了NMF的谐波约束变体。虽然有用，但谐波约束在混合信号中可能是紧缩的。我们先前已经观察到，通过引入稀疏编码步骤，使用NMF的重叠信号元素的恢复得到改善，并且在此提出将使用Hellinger距离的稀疏编码步骤并入NMF算法中。改进的AMT结果无监督NMF的报告。
Non-negative Matrix Factorisation (NMF) is a commonly used tool in many musical signal processing tasks, including Automatic Music Transcription (AMT). However unsupervised NMF is seen to be problematic in this context, and harmonically constrained variants of NMF have been proposed. While useful, the harmonic constraints may be constrictive in mixed signals. We have previously observed that recovery of overlapping signal elements using NMF is improved through introduction of a sparse coding step, and propose here the incorporation of a sparse coding step using the Hellinger distance into a NMF algorithm. Improved AMT results for unsupervised NMF are reported.