Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Christine Evers"'
Anthropocentrism and Environmental Wellbeing in AI Ethics Standards: A Scoping Review and Discussion
Publikováno v:
AI, Vol 4, Iss 4, Pp 844-874 (2023)
As AI deployment has broadened, so too has an awareness for the ethical implications and problems that may ensue from this deployment. In response, groups across multiple domains have issued AI ethics standards that rely on vague, high-level principl
Externí odkaz:
https://doaj.org/article/ca84cff2d4654c13a085b5dd544f85b3
Publikováno v:
Environmental Data Science, Vol 2 (2023)
Land cover classification (LCC) and natural disaster response (NDR) are important issues in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation (EO) imaging data for LCC and NDR often rely on
Externí odkaz:
https://doaj.org/article/79e5e9e21f87433d8083e7990c11373e
Autor:
J. M. Pérez-Lorenzo, Christine Evers, Raquel Viciana-Abad, Antonio Martínez-Colón, Patrick A. Naylor
Improving the ability to interact through voice with a robot is still a challenge especially in real environments where multiple speakers coexist. This work has evaluated a proposal based on improving the intelligibility of the voice information that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c1390a5098aa2f3980fac957910c5b4
https://eprints.soton.ac.uk/452137/
https://eprints.soton.ac.uk/452137/
Autor:
Heinrich W. Lollmann, Alexander Schmidt, Heinrich Mellmann, Hendrik Barfuss, Christine Evers, Patrick A. Naylor, Walter Kellermann
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 28:1620-1643
The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adverse
Publikováno v:
2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).
Speech enhancement is important for applications such as telecommunications, hearing aids, automatic speech recognition and voice-controlled systems. Enhancement algorithms aim to reduce interfering noise and reverberation while minimizing any speech
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63b2e0d4e94a03fb3c5003ff8e3c5984
http://hdl.handle.net/10044/1/92621
http://hdl.handle.net/10044/1/92621
Publikováno v:
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
Audio source separation is essential for many applications such as hearing aids, telecommunications, and robot audition. Subspace decomposition approaches using polynomial matrix eigenvalue decomposition (PEVD) algorithms applied to the microphone si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c401ebb4c7cef3eaef07a779422ebc6c
https://eprints.soton.ac.uk/450813/
https://eprints.soton.ac.uk/450813/
Publikováno v:
ICASSP
IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE International Conference on Acoustics, Speech and Signal Processing
Speech enhancement algorithms using polynomial matrix eigen value decomposition (PEVD) have been shown to be effective for noisy and reverberant speech. However, these algorithms do not scale well in complexity with the number of channels used in the
Publikováno v:
ICASSP
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
An essential part of any diarization system is the task of speaker segmentation which is important for many applications including speaker indexing and automatic speech recognition (ASR) in multi-speaker environments. Segmentation of overlapping spee
Publikováno v:
IEEE Signal Processing Letters
IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2019, 26 (6), pp.798-802. ⟨10.1109/LSP.2019.2908376⟩
IEEE Signal Processing Letters, 2019, 26 (6), pp.798-802. ⟨10.1109/LSP.2019.2908376⟩
IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2019, 26 (6), pp.798-802. ⟨10.1109/LSP.2019.2908376⟩
IEEE Signal Processing Letters, 2019, 26 (6), pp.798-802. ⟨10.1109/LSP.2019.2908376⟩
In this paper we address the problem of simultaneously tracking several moving audio sources, namely the problem of estimating source trajectories from a sequence of observed features. We propose to use the von Mises distribution to model audio-sourc