@MASTERSTHESIS\{IMM2008-05999,
    author       = "K. T. Andersen",
    title        = "Wind Noise Reduction in Single Channel Speech Signals",
    year         = "2008",
    month        = "feb",
    keywords     = "Non-negatiev matrix factorization, single channel speech separation",
    school       = "Department of Informatics and Mathematical Modelling",
    address      = "Richard Petersens Plads, B321, {DK-}2800 Kongens Lyngby",
    type         = "",
    note         = "Supervisor: Jan Larsen and Mikkel N. Schimdt",
    url          = "http://orbit.dtu.dk/Publications,resultSetTable.recordLink.sdirect?sp=211453",
    abstract     = "In this thesis a number of wind noise reduction techniques have been reviewed, implemented and evaluated. The focus is on reducing wind noise from speech in single channel signals. More specically a generalized version of a Spectral Subtraction method is implemented along with a Non-Stationary version that can estimate the noise even while speech is present. Also a Non-Negative Matrix Factorization method is implemented. The {PESQ} measure, different variations of the {SNR} and Noise Residual measure, and a subjective {MUSHRA} test is used to evaluate the performance of the methods. The overall conclusion is that the Non-Negative Matrix Factorization algorithm provides the best noise reduction of the investigated methods. This is based on both the perceptual and energy-based evaluation. An advantage of this method is that it does not need a Voice Activity Detector (VAD) and only assumes a-priori information about the wind noise. In fact, the method can be viewed solely as an advanced noise estimator. The downside of the algorithm is that it has a relatively high computational complexity. The Generalized Spectral Subtraction method is shown to improve the speech quality, when used together with the Non-Negative Matrix Factorization.",
    isbn_issn    = "IMM-M.Sc.-2008-18"
}