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Sparse modeling for speech and audio processing

  1. Research

    Fast fundamental frequency determination via adaptive autocorrelation

    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...

    Michael Staudacher, Viktor Steixner, Andreas Griessner and Clemens Zierhofer

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

    Published on: 24 October 2016

  2. Research

    Bayesian group sparse learning for music source separation

    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...

    Jen-Tzung Chien and Hsin-Lung Hsieh

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

    Published on: 5 July 2013

  3. Research

    High level feature extraction for the self-taught learning algorithm

    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...

    Konstantin Markov and Tomoko Matsui

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

    Published on: 9 April 2013

  4. Research

    Nonparametric Bayesian sparse factor analysis for frequency domain blind source separation without permutation ambiguity

    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 ...

    Kohei Nagira, Takuma Otsuka and Hiroshi G Okuno

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

    Published on: 22 January 2013

  5. Research

    An efficient solution to sparse linear prediction analysis of speech

    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...

    Vahid Khanagha and Khalid Daoudi

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

    Published on: 22 January 2013