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@PHDTHESIS{Thieling:1022044,
      author       = {Thieling, Lars-Hendrik},
      othercontributors = {Jax, Peter and Fingscheidt, Tim},
      title        = {{P}hase-{A}ware spectral speech enhancement using deep
                      learning techniques},
      volume       = {7},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {Shaker Verlag},
      reportid     = {RWTH-2025-09844},
      isbn         = {978-3-8191-0312-4},
      series       = {Aachen series on communication systems},
      pages        = {x, 173 Seiten : Illustrationen},
      year         = {2025},
      note         = {Druckausgabe: 2025. - Zweitveröffentlicht auf dem
                      Publikationsserver der RWTH Aachen University 2026;
                      Dissertation, Rheinisch-Westfälische Technische Hochschule
                      Aachen, 2025},
      abstract     = {In everyday environments, speech is often degraded by
                      background noise, reverberation, echo, or transmission
                      losses. These distortions reduce quality and
                      intelligibility, impairing communication. Speech enhancement
                      techniques aim to overcome these challenges by improving the
                      perceptual quality and clarity of speech under adverse
                      conditions. This dissertation advances the emerging field of
                      phase-aware speech enhancement, which extends conventional
                      magnitude-based methods by also processing the
                      often-overlooked phase spectrum. Novel concepts for deep
                      learning-based approaches are proposed and evaluated, with a
                      particular focus on phase estimation and its integration
                      into speech enhancement. Beyond theoretical investigations
                      that highlight the potential of phase processing, methods
                      for estimating the phase with deep neural networks are
                      introduced, and strategies for jointly optimizing magnitude
                      and phase estimation are proposed. Objective measures and
                      subjective listening experiments confirm the effectiveness
                      of the proposed approaches, underlining their relevance for
                      the next generation of speech enhancement systems.},
      cin          = {613310},
      ddc          = {621.3},
      cid          = {$I:(DE-82)613310_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-09844},
      url          = {https://publications.rwth-aachen.de/record/1022044},
}