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Musical Instrument Sound Multi-Excitation Model for Non-Negative Spectrogram Factorization

机译:用于非负谱图分解的乐器声音多激励模型

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This paper presents theoretical and experimental results about constrained non-negative matrix factorization (NMF) to model the excitation of the musical instruments. These excitations represent vibrating objects, while the filter represents the resonance structure of the instrument, which colors the produced sound. We propose to model the excitations as the weighted sum of harmonically constrained basis functions, whose parameters are tied across different pitches of an instrument. An NMF-based framework is used to learn the model parameters. We assume that the excitations of a well-tempered instrument should possess an identifiable characteristic structure whereas the conditions of the music scene might produce variations in the filter. In order to test the reliability of our proposal, we evaluate our method for a music transcription task in two scenarios. On the first one, comparison with state-of-the-art methods has been performed over a dataset of piano recordings obtaining more accurate results than other NMF-based algorithms. On the second one, two woodwind instrument databases have been used to demonstrate the benefits of our model in comparison with previous excitation-filter model approaches.
机译:本文介绍了有关约束非负矩阵分解(NMF)来模拟乐器激励的理论和实验结果。这些激励代表振动的物体,而滤波器代表乐器的共振结构,使产生的声音着色。我们建议将激励建模为谐波约束基函数的加权总和,其参数与乐器的不同音高相关。基于NMF的框架用于学习模型参数。我们假定,调音好的乐器的激励应具有可识别的特征结构,而音乐场景的条件可能会在滤波器中产生变化。为了测试我们建议的可靠性,我们在两种情况下评估了用于音乐转录任务的方法。在第一个方法上,已经对钢琴录音数据集进行了与最新方法的比较,从而获得比其他基于NMF的算法更准确的结果。在第二篇文章中,使用了两个木管乐器数据库来证明我们的模型与以前的激励滤波器模型方法相比的优势。

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