Skip to main content

Sparse modeling for speech and audio processing

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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