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首页> 外文期刊>Journal of Intelligent Information Systems >Estimation of Musical Sound Separation Algorithm Effectiveness Employing Neural Networks
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Estimation of Musical Sound Separation Algorithm Effectiveness Employing Neural Networks

机译：神经网络的音乐声音分离算法有效性估计

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摘要

Blind separation of musical sounds contained in sound mixtures is a challenging and difficult task. It is due to the fact that in Western music, mixed harmonic sources may be correlated with each other, i.e. their harmonic partials might be overlapping in the frequency domain if the signals remain in harmonic relation. Evaluation of the separation results is also problematic, since analysis of the energy-based error between the original signals used for mixing and the separated ones, in some cases, do not correspond with perceptual evaluation results. In this paper, four separation algorithms, engineered by the Authors, are presented. Then, musical instrument sound identification based on artificial neural networks is performed as a means of evaluating the performance of the separation algorithms. Results are discussed and conclusions are derived.
机译：混音中包含的音乐声音的盲分离是一项艰巨而艰巨的任务。这是由于这样的事实，在西方音乐中，混合谐波源可能彼此相关，即，如果信号保持谐波关系，则它们的谐波部分可能在频域中重叠。分离结果的评估也是有问题的，因为在某些情况下，用于混合的原始信号和分离的信号之间基于能量的误差的分析与感知评估结果不符。在本文中，提出了四种由作者设计的分离算法。然后，执行基于人工神经网络的乐器声音识别，作为评估分离算法性能的一种手段。讨论结果并得出结论。

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