Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Michael T. Johnson"'
Publikováno v:
IEEE Access, Vol 11, Pp 5328-5336 (2023)
Temporal modulation processing is a promising technique for improving the intelligibility and quality of speech in noise. We propose a speech enhancement algorithm that constructs the temporal envelope (TEV) in the time-frequency domain by means of a
Externí odkaz:
https://doaj.org/article/b374a666d2dd4a349542ee65c68ad018
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2019, Iss 1, Pp 1-17 (2019)
Abstract Phonetic information is one of the most essential components of a speech signal, playing an important role for many speech processing tasks. However, it is difficult to integrate phonetic information into speaker verification systems since i
Externí odkaz:
https://doaj.org/article/97c6b0c62f7a4546975a14494ce369a6
Autor:
Julie N. Oswald, Amy M. Van Cise, Angela Dassow, Taffeta Elliott, Michael T. Johnson, Andrea Ravignani, Jeffrey Podos
Publikováno v:
Applied Sciences, Vol 12, Iss 23, p 12046 (2022)
The field of bioacoustics is rapidly developing and characterized by diverse methodologies, approaches and aims. For instance, bioacoustics encompasses studies on the perception of pure tones in meticulously controlled laboratory settings, documentat
Externí odkaz:
https://doaj.org/article/0964fc3c06a748a9b7ea18af6978588b
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2019, Iss 1, Pp 1-19 (2019)
Abstract In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny’s variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations.
Externí odkaz:
https://doaj.org/article/a2d4e4f68bda48c8983e6c16ad161bbb
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2018, Iss 1, Pp 1-15 (2018)
Abstract Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. However, the problem of exploding or vanishing gradients has limited their application. In recent years, long short-term memory RNNs (LSTM RNNs) have been
Externí odkaz:
https://doaj.org/article/deb1d8f9be2e4ed2b5ffcb357447c68c
Autor:
Ebenezer Out-Nyarko, Sharon Stuart Glaeser, Michael Darre, Patrick J. Clemins, Michael T. Johnson, Yao Ren, Tomasz S. Osiejuk
Publikováno v:
Algorithms, Vol 2, Iss 4, Pp 1410-1428 (2009)
Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorpor
Externí odkaz:
https://doaj.org/article/7e9aa66f08e942388221c333534409fb
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a3cae2424f477719305452d7fd603e9
https://doi.org/10.1109/icassp43922.2022.9746585
https://doi.org/10.1109/icassp43922.2022.9746585
Publikováno v:
Welding Journal. 99:239s-245s
An innovative method was proposed to determine weld joint penetration using machine learning techniques. In our approach, the dot-structured laser images reflected from an oscillating weld pool surface were captured. Experienced welders typically eva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46f7b6e703ecc19de06600bec9d4738a
https://doi.org/10.29391/2020.99.022
https://doi.org/10.29391/2020.99.022
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 17:799-808
We propose an innovative approach to enhance welding operations by using a cyber-physical system (CPS) with layered architecture and enabling a robot to be effectively operated by its commanding human. This article focuses on the recognition of the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::583c23c53d1c3ed84aea2b9621db3416
https://doi.org/10.1109/tase.2019.2945607
https://doi.org/10.1109/tase.2019.2945607
Publikováno v:
Journal of Manufacturing Processes. 48:210-217
To combine the advantages of humans (adaptive intelligence) and robots (higher movement accuracy and physical limitation), we propose a virtual reality human-robot collaborative welding system which allows them to collaborate with each other to compl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3acc4739a6ed92d2e6a9a14df728bed
https://doi.org/10.1016/j.jmapro.2019.10.016
https://doi.org/10.1016/j.jmapro.2019.10.016