On-Line Nonnegative Matrix Factorization Method Using Recursive Least Squares for Acoustic Signal Processing Systems

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概要

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• In this paper, an on-line nonnegative matrix factorization (NMF) algorithm for acoustic signal processing systems is developed based on the recursive least squares (RLS) method. In order to develop the on-line NMF algorithm, we reformulate the NMF problem into multiple least squares (LS) normal equations, and solve the reformulated problems using RLS methods. In addition, we eliminate the irrelevant calculations based on the NMF model. The proposed algorithm has been evaluated with a well-known dataset used for NMF performance evaluation and with real acoustic signals; the results show that the proposed algorithm performs better than the conventional algorithm in on-line applications.

• （社）電子情報通信学会の論文
• 2011-10-01

著者

• Sung Koeng-mo

  Inmc Seoul National University

• LEE Seokjin

  INMC, Seoul National University

• PARK Sang

  INMC, Seoul National University

• SUNG Koeng-Mo

  INMC, Seoul National University

• Lee Seokjin

  Inmc Seoul National University

• Sung Koeng-mo

  School Of Electrical Engineering And Computer Science & Inmc Seoul National University

• Park Sang

  Inmc Seoul National University

関連論文

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• On-Line Nonnegative Matrix Factorization Method Using Recursive Least Squares for Acoustic Signal Processing Systems
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• 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

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