Majorization-minimization algorithm for smooth Itakura-Saito nonnegative matrix factorization

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors C. Févotte
Journal/Conference Name IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Paper Category
Paper Abstract Nonnegative matrix factorization (NMF) with the Itakura-Saito divergence has proven efficient for audio source separation and music transcription, where the signal power spectrogram is factored into a “dictionary” matrix times an “activation” matrix. Given the nature of audio signals it is expected that the activation coefficients exhibit smoothness along time frames. This may be enforced by penalizing the NMF objective function with an extra term reflecting smoothness of the activation coefficients. We propose a novel regularization term that solves some deficiencies of our previous work and leads to an efficient implementation using a majorization-minimization procedure.
Date of publication 2011
Code Programming Language MATLAB

Copyright Researcher II 2022