Nonnegative matrix factorization (NMF) is a matrix factorization technique that might find meaningful latent nonnegative components. Since, however, the objective function is non-convex, the source separation performance can degrade when the iterative update of the basis matrix is stuck to a poor local minimum. Most of the research updates basis iteratively to minimize certain objective function with random initialization, although a few approaches have been proposed for the systematic initialization of the basis matrix such as the singular value decomposition. In this paper, we propose a novel basis estimation method inspired by the similarity of the bases training with the vector quantization, which is similar to Linde-Buzo-Gray algorithm. Experiments of the audio source separation showed that the proposed method outperformed the NMF using random initialization by about 1.64 dB and 1.43 dB in signal-to-distortion ratio when its target sources were speech and violin, respectively.
['Kisoo Kwon', 'Jong Won Shin', 'In Kyu Choi', 'Hyung Yong Kim', 'Nam Soo Kim']
Incremental approach to NMF basis estimation for audio source separation