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Sparse modeling for speech and audio processing

  1. Content type: Research

    We present an algorithm for the estimation of fundamental frequencies in voiced audio signals. The method is based on an autocorrelation of a signal with a segment of the same signal. During operation, frequen...

    Authors: Michael Staudacher, Viktor Steixner, Andreas Griessner and Clemens Zierhofer

    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2016 2016:17

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  2. Content type: Research

    Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and th...

    Authors: Jen-Tzung Chien and Hsin-Lung Hsieh

    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2013 2013:18

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  3. Content type: Research

    Availability of large amounts of raw unlabeled data has sparked the recent surge in semi-supervised learning research. In most works, however, it is assumed that labeled and unlabeled data come from the same d...

    Authors: Konstantin Markov and Tomoko Matsui

    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2013 2013:6

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  4. Content type: Research

    Blind source separation (BSS) and sound activity detection (SAD) from a sound source mixture with minimum prior information are two major requirements for computational auditory scene analysis that recognizes ...

    Authors: Kohei Nagira, Takuma Otsuka and Hiroshi G Okuno

    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2013 2013:4

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  5. Content type: Research

    We propose an efficient solution to the problem of sparse linear prediction analysis of the speech signal. Our method is based on minimization of a weighted l2-norm of the prediction error. The weighting function...

    Authors: Vahid Khanagha and Khalid Daoudi

    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2013 2013:3

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