@CONFERENCE\{IMM2006-04511,
    author       = "M. N. Schmidt and R. K. Olsson",
    title        = "Single-Channel Speech Separation using Sparse Non-Negative Matrix Factorization",
    year         = "2006",
    month        = "sep",
    booktitle    = "Interspeech",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4511-full.html",
    abstract     = "We apply machine learning techniques to the problem of separating
multiple speech sources from a single microphone recording.
The method of choice is a sparse non-negative matrix factorization
algorithm, which in an unsupervised manner can learn sparse representations
of the data. This is applied to the learning of personalized
dictionaries from a speech corpus, which in turn are used
to separate the audio stream into its components. We show that
computational savings can be achieved by segmenting the training
data on a phoneme level. To split the data, a conventional speech
recognizer is used. The performance of the unsupervised and supervised
adaptation schemes result in significant improvements in
terms of the target-to-masker ratio."
}