Academic literature on the topic 'Near-End listening enhancement'
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Journal articles on the topic "Near-End listening enhancement":
Taal, Cees H., Jesper Jensen, and Arne Leijon. "On Optimal Linear Filtering of Speech for Near-End Listening Enhancement." IEEE Signal Processing Letters 20, no. 3 (March 2013): 225–28. http://dx.doi.org/10.1109/lsp.2013.2240297.
Rennies, J., A. Pusch, H. Schepker, and 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, no. 4 (October 2018): EL315—EL321. http://dx.doi.org/10.1121/1.5064956.
Rennies, Jan, Henning Schepker, David Huelsmeier, Jakob H. Drefs, and Simon Doclo. "Evaluating near-end listening enhancement in noise for normal-hearing and hearing-impaired listeners." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 4023. http://dx.doi.org/10.1121/1.4989261.
Li, Gang, Ruimin Hu, Xiaochen Wang, and Rui Zhang. "A near-end listening enhancement system by RNN-based noise cancellation and speech modification." Multimedia Tools and Applications 78, no. 11 (December 5, 2018): 15483–505. http://dx.doi.org/10.1007/s11042-018-6947-8.
Rennies, Jan, Jakob Drefs, David Hülsmeier, Henning Schepker, and 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, no. 4 (April 2017): 2526–37. http://dx.doi.org/10.1121/1.4979591.
Fallah, Ali, and Steven van de Par. "A Speech Preprocessing Method Based on Perceptually Optimized Envelope Processing to Increase Intelligibility in Reverberant Environments." Applied Sciences 11, no. 22 (November 15, 2021): 10788. http://dx.doi.org/10.3390/app112210788.
Fuglsig, Andreas Jonas, Jesper Jensen, Zheng-Hua Tan, Lars Søndergaard Bertelsen, Jens Christian Lindof, and 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.
Celkan, Prof Dr Gul. "From the Editor." New Trends and Issues Proceedings on Humanities and Social Sciences 2, no. 3 (December 7, 2016). http://dx.doi.org/10.18844/prosoc.v2i3.1244.
Loess, Nicholas. "Augmentation and Improvisation." M/C Journal 16, no. 6 (November 7, 2013). http://dx.doi.org/10.5204/mcj.739.
Dissertations / Theses on the topic "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.
Gentet, 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.
Speech 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
Book chapters on the topic "Near-End listening enhancement":
Herasimovich, Vadzim, Alexey Petrovsky, Vladislav Avramov, and 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.
Conference papers on the topic "Near-End listening enhancement":
Niermann, Markus, Peter Jax, and 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.
Chermaz, Carol, Cassia Valentini-Botinhao, Henning Schepker, and 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.
Chermaz, Carol, and 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.
Niermann, Markus, Peter Jax, and 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.
Lavanya, T., K. Mrinalini, P. Vijayalakshmi, and 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.
Schepker, Henning, David Hülsmeier, Jan Rennies, and 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.
Zorilă, Tudor-Cătălin, and 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.