Auswahl der wissenschaftlichen Literatur zum Thema „Near-End listening enhancement“
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Zeitschriftenartikel zum Thema "Near-End listening enhancement"
Taal, Cees H., Jesper Jensen und Arne Leijon. „On Optimal Linear Filtering of Speech for Near-End Listening Enhancement“. IEEE Signal Processing Letters 20, Nr. 3 (März 2013): 225–28. http://dx.doi.org/10.1109/lsp.2013.2240297.
Der volle Inhalt der QuelleRennies, J., A. Pusch, H. Schepker und S. Doclo. „Evaluation of a near-end listening enhancement algorithm by combined speech intelligibility and listening effort measurements“. Journal of the Acoustical Society of America 144, Nr. 4 (Oktober 2018): EL315—EL321. http://dx.doi.org/10.1121/1.5064956.
Der volle Inhalt der QuelleRennies, Jan, Henning Schepker, David Huelsmeier, Jakob H. Drefs und Simon Doclo. „Evaluating near-end listening enhancement in noise for normal-hearing and hearing-impaired listeners“. Journal of the Acoustical Society of America 141, Nr. 5 (Mai 2017): 4023. http://dx.doi.org/10.1121/1.4989261.
Der volle Inhalt der QuelleLi, Gang, Ruimin Hu, Xiaochen Wang und Rui Zhang. „A near-end listening enhancement system by RNN-based noise cancellation and speech modification“. Multimedia Tools and Applications 78, Nr. 11 (05.12.2018): 15483–505. http://dx.doi.org/10.1007/s11042-018-6947-8.
Der volle Inhalt der QuelleRennies, Jan, Jakob Drefs, David Hülsmeier, Henning Schepker und Simon Doclo. „Extension and evaluation of a near-end listening enhancement algorithm for listeners with normal and impaired hearing“. Journal of the Acoustical Society of America 141, Nr. 4 (April 2017): 2526–37. http://dx.doi.org/10.1121/1.4979591.
Der volle Inhalt der QuelleFallah, Ali, und Steven van de Par. „A Speech Preprocessing Method Based on Perceptually Optimized Envelope Processing to Increase Intelligibility in Reverberant Environments“. Applied Sciences 11, Nr. 22 (15.11.2021): 10788. http://dx.doi.org/10.3390/app112210788.
Der volle Inhalt der QuelleFuglsig, Andreas Jonas, Jesper Jensen, Zheng-Hua Tan, Lars Søndergaard Bertelsen, Jens Christian Lindof und Jan Østergaard. „Minimum Processing Near-end Listening Enhancement“. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 1–13. http://dx.doi.org/10.1109/taslp.2023.3282094.
Der volle Inhalt der QuelleCelkan, Prof Dr Gul. „From the Editor“. New Trends and Issues Proceedings on Humanities and Social Sciences 2, Nr. 3 (07.12.2016). http://dx.doi.org/10.18844/prosoc.v2i3.1244.
Der volle Inhalt der QuelleLoess, Nicholas. „Augmentation and Improvisation“. M/C Journal 16, Nr. 6 (07.11.2013). http://dx.doi.org/10.5204/mcj.739.
Der volle Inhalt der QuelleDissertationen zum Thema "Near-End listening enhancement"
Sauert, Bastian [Verfasser]. „Near-end listening enhancement : theory and application / Bastian Sauert“. Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1057037257/34.
Der volle Inhalt der QuelleGentet, Enguerrand. „Amélioration de l'intelligibilité de signaux audio de parole en contexte bruité automobile“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT008.
Der volle Inhalt der QuelleSpeech is nowadays present in a number of in-car applications ranging from hands-free communications, radio programs to speech synthesis messages from the various car devices.However, despite the steady car manufacturing progress, significant noise still remains in the car interior that leads to a loss of intelligibility of speech signals. The PhD work aims at developping speech reinforcement tools in order to process the signals before they are played in a noisy in-car environment.A highly effective speech reinforcement approach is to use a frequency equalizer to optimize an intelligibility criterion : the Speech Intelligibility Index (SII). To facilitate optimization, current methods are based on approximations of the criterion. In addition, by concentrating the spectral energy of the signal in areas where the ear is more sensitive, these methods increase the perceived volume which can deteriorate the user experience. Thus, in addition to proposing an exact method of solving the SII maximization problem, our work proposes to introduce and study the influence of a new perceptual constraint in order to maintain the signals at their perceived level.The popularization of machine learning approaches pushes to learn speech reinforcement processings from examples naturally produced in noise (Lombard speech), or by over-articulation (clear speech). Current work fails to achieve intelligibility gains as significant as with natural modification, and we believe that the many temporal aspects neglect may be partially responsible. Our work therefore proposes to deepen these approaches by exploiting learning models and pre-processings adapted to long duration sequences. We also propose a new modeling of the speech rate modifications that directly fits in the machine learning model which had never been done before
Buchteile zum Thema "Near-End listening enhancement"
Herasimovich, Vadzim, Alexey Petrovsky, Vladislav Avramov und Alexander Petrovsky. „Audio/Speech Coding Based on the Perceptual Sparse Representation of the Signal with DAE Neural Network Quantizer and Near-End Listening Enhancement“. In Cryptology and Network Security, 109–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98678-4_13.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Near-End listening enhancement"
Niermann, Markus, Peter Jax und Peter Vary. „Joint Near-End Listening Enhancement and far-end noise reduction“. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953102.
Der volle Inhalt der QuelleChermaz, Carol, Cassia Valentini-Botinhao, Henning Schepker und Simon King. „Evaluating Near End Listening Enhancement Algorithms in Realistic Environments“. In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1800.
Der volle Inhalt der QuelleChermaz, Carol, und Simon King. „A Sound Engineering Approach to Near End Listening Enhancement“. In Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2748.
Der volle Inhalt der QuelleNiermann, Markus, Peter Jax und Peter Vary. „Near-end listening enhancement by noise-inverse speech shaping“. In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760677.
Der volle Inhalt der QuelleLavanya, T., K. Mrinalini, P. Vijayalakshmi und T. Nagarajan. „Histogram Matching based Optimized Energy Redistribution for Near End Listening Enhancement“. In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929292.
Der volle Inhalt der QuelleSchepker, Henning, David Hülsmeier, Jan Rennies und Simon Doclo. „Model-based integration of reverberation for noise-adaptive near-end listening enhancement“. In Interspeech 2015. ISCA: ISCA, 2015. http://dx.doi.org/10.21437/interspeech.2015-30.
Der volle Inhalt der QuelleZorilă, Tudor-Cătălin, und Yannis Stylianou. „On the Quality and Intelligibility of Noisy Speech Processed for Near-End Listening Enhancement“. In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1225.
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