Browsing by Author "Janský Jakub"
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- ItemA local model of relative transfer functions involving sparsity(Springer Verlag, 2015-01-01) Koldovský Zbyněk; Janský Jakub; Nesta F.
- ItemAdaptive blind audio source extraction supervised by dominant speaker identification using x-vectors(IEEE, 2020-01-01) Janský Jakub; Málek Jiří; Čmejla Jaroslav; Kounovský Tomáš; Koldovský Zbyněk; Žďánský Jindřich
- ItemAdvanced semi-blind speaker extraction and tracking implemented in experimental device with revolving dense microphone array(International Speech Communication Association, 2021-01-01) Čmejla Jaroslav; Kounovský Tomáš; Janský Jakub; Málek Jiří; Rozkovec Martin; Koldovský Zbyněk
- ItemAuxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker(Springer, 2021-01-01) Janský Jakub; Koldovský Zbyněk; Málek Jiří; Kounovský Tomáš; Čmejla Jaroslav
- ItemBlind extraction of moving audio source in a challenging environment supported by speaker identification via X-vectors(IEEE, 2021-01-01) Málek Jiří; Janský Jakub; Kounovský Tomáš; Koldovský Zbyněk; Žďánský Jindřich
- ItemBlind Source Separation Using Incomplete De-Mixing Transform with a Precise Approach for Selecting Constrained Sets of Frequencies(2018-01-01) Janský Jakub; Koldovský Zbyněk
- ItemCHiME4: Multichannel Enhancement Using Beamforming Driven by a DNN-based Voice Activity Detector(2016-01-01) Koldovský Zbyněk; Málek Jiří; Boháč Marek; Janský Jakub
- ItemA Computationally Cheaper Method for Blind Speech Separation Based On AuxIVA and Incomplete Demixing Transform(Institute of Electrical and Electronics Engineers Inc., 2016-01-01) Janský Jakub; Koldovský Zbyněk; Ono Nobutaka
- ItemExtraction of Independent Vector Component from Underdetermined Mixtures through Block-Wise Determined Modeling(IEEE, 2019-01-01) Koldovský Zbyněk; Málek Jiří; Janský Jakub
- ItemLinear acoustic echo cancellation using deep neural networks and convex reconstruction of incomplete transfer function(Institute of Electrical and Electronics Engineers Inc., 2017-01-01) Janský Jakub; Boháč Marek; Koldovský Zbyněk; Müller MichaelLinear acoustic path estimation for acoustic echo cancellation is difficult during periods where the near-end signal (speech) is active. In this paper, we assume that the impulse response is sparse. There are many algorithms that solve the problem of estimating sparse impulse response in the time domain. In this paper, we propose algorithms working in the time-frequency domain. In our approach, it is assumed that the respective transfer function can be estimated only for those frequencies where the near-end signal is not active. First, a deep neural network trained on mixed signals is used to detect the activity of the near-end signal. In frequencies where no activity is detected, the acoustic transfer function is estimated using conventional frequency domain least squares. This results in an incomplete transfer function (ITF) estimate. The completion is done through finding the sparsest representation of the ITF in the time domain. This can be done adaptively using the soft-threshold function, which is applied in the time domain. To achieve improved accuracy, oversampling can be used.