Acoustic model

Results: 178



#Item
1ACOUSTIC MODEL TRANSFORMATIONS BASED ON RANDOM PROJECTIONS Tetsuya Takiguchi, Mariko Yoshii, Yasuo Ariki Jeff Bilmes  Graduate School of System Informatics

ACOUSTIC MODEL TRANSFORMATIONS BASED ON RANDOM PROJECTIONS Tetsuya Takiguchi, Mariko Yoshii, Yasuo Ariki Jeff Bilmes Graduate School of System Informatics

Add to Reading List

Source URL: www.me.cs.scitec.kobe-u.ac.jp

Language: English - Date: 2018-02-04 05:46:56
    2Adaptation of an acoustic propagation model to the parallel architecture of a graphics processor Emanuel Ey

    Adaptation of an acoustic propagation model to the parallel architecture of a graphics processor Emanuel Ey

    Add to Reading List

    Source URL: www.siplab.fct.ualg.pt

    Language: Portuguese - Date: 2013-12-27 03:51:55
      3MODELLING ACOUSTIC FEATURE DEPENDENCIES WITH ARTIFICIAL NEURAL NETWORKS: TRAJECTORY-RNADE Benigno Uria1 1  Iain Murray1

      MODELLING ACOUSTIC FEATURE DEPENDENCIES WITH ARTIFICIAL NEURAL NETWORKS: TRAJECTORY-RNADE Benigno Uria1 1 Iain Murray1

      Add to Reading List

      Source URL: www.cstr.inf.ed.ac.uk

      Language: English - Date: 2015-09-29 11:06:25
      4Approach to Real Time Encoding of Audio Samples A DSP Realization of the MPEG Algorithm ME 235 Professor Steve Kraft University of California Berkeley

      Approach to Real Time Encoding of Audio Samples A DSP Realization of the MPEG Algorithm ME 235 Professor Steve Kraft University of California Berkeley

      Add to Reading List

      Source URL: www.mp3-tech.org

      Language: English - Date: 2009-06-28 10:51:53
      5DEEP NEURAL NETWORKS EMPLOYING MULTI-TASK LEARNING AND STACKED BOTTLENECK FEATURES FOR SPEECH SYNTHESIS Zhizheng Wu Cassia Valentini-Botinhao

      DEEP NEURAL NETWORKS EMPLOYING MULTI-TASK LEARNING AND STACKED BOTTLENECK FEATURES FOR SPEECH SYNTHESIS Zhizheng Wu Cassia Valentini-Botinhao

      Add to Reading List

      Source URL: www.cstr.inf.ed.ac.uk

      Language: English - Date: 2015-09-29 11:06:25
      6GLIDERS AS A COMPONENT OF FUTURE OBSERVING SYSTEMS Pierre Testor(1), Gary Meyers(2), Chari Pattiaratchi(3), Ralf Bachmayer(4), Dan Hayes(5), Sylvie Pouliquen(6), Loic Petit de la Villeon(6), Thierry Carval(6), Alexandre

      GLIDERS AS A COMPONENT OF FUTURE OBSERVING SYSTEMS Pierre Testor(1), Gary Meyers(2), Chari Pattiaratchi(3), Ralf Bachmayer(4), Dan Hayes(5), Sylvie Pouliquen(6), Loic Petit de la Villeon(6), Thierry Carval(6), Alexandre

      Add to Reading List

      Source URL: www.ioccp.org

      Language: English - Date: 2016-04-05 04:56:08
      7Seismo-acoustic ray model benchmarking against experimental tank data Orlando Camargo Rodrı´gueza) LARSyS, Campus de Gambelas, Universidade do Algarve, PTFaro, Portugal  Jon M. Collis

      Seismo-acoustic ray model benchmarking against experimental tank data Orlando Camargo Rodrı´gueza) LARSyS, Campus de Gambelas, Universidade do Algarve, PTFaro, Portugal Jon M. Collis

      Add to Reading List

      Source URL: www.siplab.fct.ualg.pt

      Language: English - Date: 2012-12-19 07:39:10
      8A GENERALIZED BAYESIAN MODEL FOR TRACKING LONG METRICAL CYCLES IN ACOUSTIC MUSIC SIGNALS Ajay Srinivasamurthy ⋆ , Andre Holzapfel † , Ali Taylan Cemgil ‡ , Xavier Serra ⋆ ⋆  Music Technology Group, Universitat

      A GENERALIZED BAYESIAN MODEL FOR TRACKING LONG METRICAL CYCLES IN ACOUSTIC MUSIC SIGNALS Ajay Srinivasamurthy ⋆ , Andre Holzapfel † , Ali Taylan Cemgil ‡ , Xavier Serra ⋆ ⋆ Music Technology Group, Universitat

      Add to Reading List

      Source URL: www.rhythmos.org

      Language: English - Date: 2016-02-09 05:50:48
      9758  IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 38, NO. 4, OCTOBER 2013 Linking Acoustic Communications and Network Performance: Integration and Experimentation

      758 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 38, NO. 4, OCTOBER 2013 Linking Acoustic Communications and Network Performance: Integration and Experimentation

      Add to Reading List

      Source URL: www.siplab.fct.ualg.pt

      Language: English - Date: 2013-10-20 12:32:13
      10Minimum trajectory error training for deep neural networks, combined with stacked bottleneck features Zhizheng Wu Simon King

      Minimum trajectory error training for deep neural networks, combined with stacked bottleneck features Zhizheng Wu Simon King

      Add to Reading List

      Source URL: www.cstr.inf.ed.ac.uk

      Language: English