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pro vyhledávání: '"Halimeh, Mhd Modar"'
Autor:
Wechsler, Julian, Chetupalli, Srikanth Raj, Halimeh, Mhd Modar, Thiergart, Oliver, Habets, Emanuël A. P.
Capturing audio signals with specific directivity patterns is essential in speech communication. This study presents a deep neural network (DNN)-based approach to directional filtering, alleviating the need for explicit signal models. More specifical
Externí odkaz:
http://arxiv.org/abs/2409.13502
Dialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-related applications. In this paper, ConcateNet for dialogue separatio
Externí odkaz:
http://arxiv.org/abs/2408.08729
Autor:
Torcoli, Matteo, Halimeh, Mhd Modar, Leitz, Thomas, Grewe, Yannik, Kratschmer, Michael, Neugebauer, Bernhard, Murtaza, Adrian, Fuchs, Harald, Habets, Emanuël A. P.
The introduction and regulation of loudness in broadcasting and streaming brought clear benefits to the audience, e.g., a level of uniformity across programs and channels. Yet, speech loudness is frequently reported as being too low in certain passag
Externí odkaz:
http://arxiv.org/abs/2405.17364
Autor:
Torcoli, Matteo, Wu, Chih-Wei, Dick, Sascha, Williams, Phillip A., Halimeh, Mhd Modar, Wolcott, William, Habets, Emanuel A. P.
Research into the prediction and analysis of perceived audio quality is hampered by the scarcity of openly available datasets of audio signals accompanied by corresponding subjective quality scores. To address this problem, we present the Open Datase
Externí odkaz:
http://arxiv.org/abs/2401.00197
In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial.
Externí odkaz:
http://arxiv.org/abs/2210.15512
Autor:
Halimeh, Mhd Modar, Kellermann, Walter
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:
http://arxiv.org/abs/2108.03130
Publikováno v:
In Signal Processing July 2024 220
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:
http://arxiv.org/abs/2012.08867
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial.
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