Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Mhd Modar Halimeh"'
Publikováno v:
Artificial Intelligence in HCI ISBN: 9783031056420
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::941aeca0897330a710ce256d855263f9
https://doi.org/10.1007/978-3-031-05643-7_39
https://doi.org/10.1007/978-3-031-05643-7_39
Publikováno v:
IEEE Signal Processing Letters. 26:1827-1831
In this letter, we introduce a novel approach for nonlinear acoustic echo cancellation. The proposed approach uses the principle of transfer learning to train a neural network that approximates the nonlinear function responsible for the nonlinear dis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb131604063a54e8206338464de00525
https://doi.org/10.1109/lsp.2019.2951311
https://doi.org/10.1109/lsp.2019.2951311
Autor:
Mhd Modar Halimeh, Walter Kellermann
In this contribution, we present a novel online approach to multichannel speech enhancement. The proposed method estimates the enhanced signal through a filter-and-sum framework. More specifically, complex-valued masks are estimated by a deep complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bf3e11f74bf6cc8f8221f4c34c83887
We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter. The proposed algorithm overcomes the well-known limitations of Kalman filter-based a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aaa90d83c08a9d0aa6f5bda5dd579878
http://arxiv.org/abs/2012.08867
http://arxiv.org/abs/2012.08867
Autor:
Walter Kellermann, Mhd Modar Halimeh
Publikováno v:
ICASSP
While a common approach to address nonlinear distortions, emitted by multiple loudspeakers and observed by multiple microphones, is to use post-filtering techniques, this paper proposes a cooperative strategy to rather model and then cancel such dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::777bbdf25a80c8cf8e88ec7d1b922fb6
https://doi.org/10.1109/icassp40776.2020.9054541
https://doi.org/10.1109/icassp40776.2020.9054541
Publikováno v:
ICASSP
In this contribution, we introduce a novel approach to noise-robust acoustic echo cancellation employing a complex-valued Deep Neural Network (DNN) for postfiltering. In a first step, early linear echo components are removed using a double-talk robus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d300b99f0d9e573e5745e507b34cf8b1
Publikováno v:
EUSIPCO
In this paper, we introduce a Bayesian framework to perform model selection for nonlinear acoustic echo cancellation. This is especially important for scenarios where the functional form of the underlying nonlinear distortion is time-varying and/or i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da201a5e5d875bf1a53ed652153fdc16
https://doi.org/10.23919/eusipco.2019.8902673
https://doi.org/10.23919/eusipco.2019.8902673
Publikováno v:
ICASSP
In this paper, we propose a sequential training algorithm for feed-forward neural networks based on particle filtering. The proposed algorithm uses variational learning to tailor a proposal density by minimizing the variational energy. This density i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1898da9fa539959e4e78fb9c97a36c59
https://doi.org/10.1109/icassp.2019.8683886
https://doi.org/10.1109/icassp.2019.8683886
Publikováno v:
EUSIPCO
A resampling scheme is proposed for use with Sequential Monte Carlo (SMC)-based Probability Hypothesis Density (PHD) filters. It consists of two steps, first, regions of interest are identified, then an evolutionary resampling is applied for each reg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0b993a1902963e240cd29346d42717c
https://doi.org/10.23919/eusipco.2018.8553478
https://doi.org/10.23919/eusipco.2018.8553478