Gotowa bibliografia na temat „Near-End listening enhancement”
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Artykuły w czasopismach na temat "Near-End listening enhancement"
Taal, Cees H., Jesper Jensen i Arne Leijon. "On Optimal Linear Filtering of Speech for Near-End Listening Enhancement". IEEE Signal Processing Letters 20, nr 3 (marzec 2013): 225–28. http://dx.doi.org/10.1109/lsp.2013.2240297.
Pełny tekst źródłaRennies, J., A. Pusch, H. Schepker i 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 (październik 2018): EL315—EL321. http://dx.doi.org/10.1121/1.5064956.
Pełny tekst źródłaRennies, Jan, Henning Schepker, David Huelsmeier, Jakob H. Drefs i 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 (maj 2017): 4023. http://dx.doi.org/10.1121/1.4989261.
Pełny tekst źródłaLi, Gang, Ruimin Hu, Xiaochen Wang i Rui Zhang. "A near-end listening enhancement system by RNN-based noise cancellation and speech modification". Multimedia Tools and Applications 78, nr 11 (5.12.2018): 15483–505. http://dx.doi.org/10.1007/s11042-018-6947-8.
Pełny tekst źródłaRennies, Jan, Jakob Drefs, David Hülsmeier, Henning Schepker i 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 (kwiecień 2017): 2526–37. http://dx.doi.org/10.1121/1.4979591.
Pełny tekst źródłaFallah, Ali, i 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.
Pełny tekst źródłaFuglsig, Andreas Jonas, Jesper Jensen, Zheng-Hua Tan, Lars Søndergaard Bertelsen, Jens Christian Lindof i 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.
Pełny tekst źródłaCelkan, Prof Dr Gul. "From the Editor". New Trends and Issues Proceedings on Humanities and Social Sciences 2, nr 3 (7.12.2016). http://dx.doi.org/10.18844/prosoc.v2i3.1244.
Pełny tekst źródłaLoess, Nicholas. "Augmentation and Improvisation". M/C Journal 16, nr 6 (7.11.2013). http://dx.doi.org/10.5204/mcj.739.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaGentet, 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.
Pełny tekst źródłaSpeech 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
Części książek na temat "Near-End listening enhancement"
Herasimovich, Vadzim, Alexey Petrovsky, Vladislav Avramov i Alexander Petrovsky. "Audio/Speech Coding Based on the Perceptual Sparse Representation of the Signal with DAE Neural Network Quantizer and Near-End Listening Enhancement". W Cryptology and Network Security, 109–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98678-4_13.
Pełny tekst źródłaStreszczenia konferencji na temat "Near-End listening enhancement"
Niermann, Markus, Peter Jax i Peter Vary. "Joint Near-End Listening Enhancement and far-end noise reduction". W 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953102.
Pełny tekst źródłaChermaz, Carol, Cassia Valentini-Botinhao, Henning Schepker i Simon King. "Evaluating Near End Listening Enhancement Algorithms in Realistic Environments". W Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1800.
Pełny tekst źródłaChermaz, Carol, i Simon King. "A Sound Engineering Approach to Near End Listening Enhancement". W Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2748.
Pełny tekst źródłaNiermann, Markus, Peter Jax i Peter Vary. "Near-end listening enhancement by noise-inverse speech shaping". W 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760677.
Pełny tekst źródłaLavanya, T., K. Mrinalini, P. Vijayalakshmi i T. Nagarajan. "Histogram Matching based Optimized Energy Redistribution for Near End Listening Enhancement". W TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929292.
Pełny tekst źródłaSchepker, Henning, David Hülsmeier, Jan Rennies i Simon Doclo. "Model-based integration of reverberation for noise-adaptive near-end listening enhancement". W Interspeech 2015. ISCA: ISCA, 2015. http://dx.doi.org/10.21437/interspeech.2015-30.
Pełny tekst źródłaZorilă, Tudor-Cătălin, i Yannis Stylianou. "On the Quality and Intelligibility of Noisy Speech Processed for Near-End Listening Enhancement". W Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1225.
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