@inproceedings{02642b7cdaac40d6b7dec545e439f284,
title = "Introducing a simple fusion framework for audio source separation",
abstract = "We propose in this paper a simple fusion framework for un-derdetermined audio source separation. This framework can be applied to a wide variety of source separation algorithms providing that they estimate time-frequency masks. Fusion principles have been successfully implemented for classification tasks. Although it is similar to classification, audio source separation does not usually take advantage of such principles. We thus introduce some general fusion rules inspired by classification and we evaluate them in the context of voice extraction. Experimental results are promising as our proposed fusion rule can improve separation results up to 1 dB in SDR.",
keywords = "audio source separation, data fusion, machine learning, nonnegative matrix factorization",
author = "Xabier Jaureguiberry and Gael Richard and Pierre Leveau and Romain Hennequin and Emmanuel Vincent",
year = "2013",
month = dec,
day = "1",
doi = "10.1109/MLSP.2013.6661930",
language = "English",
isbn = "9781479911806",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
booktitle = "2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013",
note = "2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013 ; Conference date: 22-09-2013 Through 25-09-2013",
}