IEICE Trans - Clustering Algorithm for Unsupervised Monaural Musical Sound Separation Based on Non-negative Matrix Factorization

Clustering Algorithm for Unsupervised Monaural Musical Sound Separation Based on Non-negative Matrix Factorization

Sang Ha PARK  Seokjin LEE  Koeng-Mo SUNG

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E95-A   No.4   pp.818-823
Publication Date: 2012/04/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E95.A.818
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Engineering Acoustics
Keyword:
non-negative matrix factorization,  clustering,  musical sound source separation,

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Summary:
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.