Initialization of nonnegative matrix factorization dictionaries for single channel source separation

Grais, Emad Mounir and Erdoğan, Hakan (2013) Initialization of nonnegative matrix factorization dictionaries for single channel source separation. In: 21st IEEE Conference on Signal Processing and Communications Applications (SIU2013), Haspolat, Turkey

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In this work, we study different initialization methods for the nonnegative matrix factorization (NMF) dictionaries or bases. There is a need for good initializations for NMF dictionary because NMF decomposition is a non-convex problem which has many local minima. The effect of the initialization of NMF is evaluated in this work on audio source separation applications. In supervised audio source separation, NMF is used to train a set of basis vectors (basis matrix) for each source in an iterative fashion. Then NMF is used to decompose the mixed signal spectrogram as a weighted linear combination of the trained basis vectors for all sources in the mixed signal. The estimate for each source is computed by summing the decomposition terms that include its corresponding trained bases. In this work, we use principal component analysis (PCA), spherical K-means, and fuzzy C-means (FCM) to initialize the NMF basis matrices during the training procedures. Experimental results show that, better initialization for NMF bases gives better audio separation performance than using NMF with random initialization.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Nonnegative matrix factorization; data clustering; dictionary learning; fuzzy clustering; principal component analysis; single channel source separation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Emad Mounir Grais Girgis
Date Deposited: 26 Jul 2013 16:02
Last Modified: 26 Apr 2022 09:10

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