no code implementations • 23 Sep 2023 • Aashish Gottipati, Sami Khairy, Gabriel Mittag, Vishak Gopal, Ross Cutler
In this work, we tackle the problem of bandwidth estimation (BWE) for real-time communication systems; however, in contrast to previous works, we leverage the vast efforts of prior heuristic-based BWE methods and synergize these approaches with deep learning-based techniques.
1 code implementation • 22 Mar 2023 • Gabriel Mittag, Babak Naderi, Vishak Gopal, Ross Cutler
Using these features together with VMAF core features, our proposed model achieves a PCC of 0. 99 on the validation set.
1 code implementation • 23 Apr 2021 • Gabriel Mittag, Sebastian Möller
Further, we show that the reliability of deep learning-based naturalness prediction can be improved by transfer learning from speech quality prediction models that are trained on objective POLQA scores.
2 code implementations • 20 Apr 2021 • Gabriel Mittag, Saman Zadtootaghaj, Thilo Michael, Babak Naderi, Sebastian Möller
The ground truth used for training image, video, or speech quality prediction models is based on the Mean Opinion Scores (MOS) obtained from subjective experiments.
1 code implementation • 19 Apr 2021 • Gabriel Mittag, Babak Naderi, Assmaa Chehadi, Sebastian Möller
In this paper, we present an update to the NISQA speech quality prediction model that is focused on distortions that occur in communication networks.