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A quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on the short-time Fourier or Cosine transforms, the ability to learn transforms from raw data attracts increasing attention. However, this adds an important computational overhead. When assumed orthogonal (like the Fourier or Cosine transforms), learning the transform yields a nonconvex optimization problem on the
doi:10.1109/icassp.2019.8683291
dblp:conf/icassp/AblinFWGF19
fatcat:z5h4nucvy5fbvi6e3cfvjbau6y