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首页> 外文期刊>IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences >Clustering Algorithm for Unsupervised Monaural Musical Sound Separation Based on Non-negative Matrix Factorization
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Clustering Algorithm for Unsupervised Monaural Musical Sound Separation Based on Non-negative Matrix Factorization

机译：基于非负矩阵分解的无监督单声道音乐分离的聚类算法

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摘要

Non-negative matrix factorization (NMF) is widely used for monaural musical sound source separation because of its efficiency and good performance. However, an additional clustering process is required because the musical sound mixture is separated into more signals than the number of musical tracks during NMF separation. In the conventional method, manual clustering or training-based clustering is performed with an additional learning process. Recently, a clustering algorithm based on the mel-frequency cepstrum coefficient (MFCC) was proposed for unsupervised clustering. However, MFCC clustering supplies limited information for clustering. In this paper, we propose various timbre features for unsupervised clustering and a clustering algorithm with these features. Simulation experiments are carried out using various musical sound mixtures. The results indicate that the proposed method improves clustering performance, as compared to conventional MFCC-based clustering.
机译：非负矩阵分解（NMF）由于其效率高和性能好而被广泛用于单声道音乐声源分离。但是，由于在NMF分离过程中音乐声音混合被分离为比音乐声轨数目更多的信号，因此需要附加的聚类过程。在传统方法中，手动聚类或基于训练的聚类是通过额外的学习过程执行的。最近，提出了一种基于梅尔频率倒谱系数（MFCC）的聚类算法，用于无监督聚类。但是，MFCC群集为群集提供有限的信息。在本文中，我们提出了用于无监督聚类的各种音色特征以及具有这些特征的聚类算法。模拟实验是使用各种音乐混音进行的。结果表明，与传统的基于MFCC的聚类相比，该方法提高了聚类性能。

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