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Inventi Impact - Signal Processing

Articles

  • Inventi:esp/68/14
    SINGLE-CHANNEL NOISE REDUCTION USING UNIFIED JOINT DIAGONALIZATION AND OPTIMAL FILTERING
    Sidsel Marie Norholm, Jacob Benesty, Jesper Rindom Jensen, Mads Græsboll Christensen

    In this paper, the important problem of single-channel noise reduction is treated from a new perspective. The problem is posed as a filtering problem based on joint diagonalization of the covariance matrices of the desired and noise signals. More specifically, the eigenvectors from the joint diagonalization corresponding to the least significant eigenvalues are used to form a filter, which effectively estimates the noise when applied to the observed signal. This estimate is then subtracted from the observed signal to form an estimate of the desired signal, i.e., the speech signal. In doing this, we consider two cases, where, respectively, no distortion and distortion are incurred on the desired signal. The former can be achieved when the covariance matrix of the desired signal is rank deficient, which is the case, for example, for voiced speech. In the latter case, the covariance matrix of the desired signal is full rank, as is the case, for example, in unvoiced speech. Here, the amount of distortion incurred is controlled via a simple, integer parameter, and the more distortion allowed, the higher the output signal-to-noise ratio (SNR). Simulations demonstrate the properties of the two solutions. In the distortionless case, the proposed filter achieves only a slightly worse output SNR, compared to the Wiener filter, along with no signal distortion. Moreover, when distortion is allowed, it is possible to achieve higher output SNRs compared to the Wiener filter. Alternatively, when a lower output SNR is accepted, a filter with less signal distortion than the Wiener filter can be constructed

    How to Cite this Article
    CC Compliant Citation: Nørholm et al.: Single-channel noise reduction using unified joint diagonalization and optimal filtering. EURASIP Journal on Advances in Signal Processing 2014 2014:37, doi:10.1186/1687-6180-2014-37. © 2014 Nørholm et al.; licensee Springer. This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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