Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Barker, Jon"'
Autor:
Ranzinger, Mike, Barker, Jon, Heinrich, Greg, Molchanov, Pavlo, Catanzaro, Bryan, Tao, Andrew
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, termed "agglomerative models." We build upon this body of work by studyi
Externí odkaz:
http://arxiv.org/abs/2410.01680
Autor:
Dai, Wenliang, Lee, Nayeon, Wang, Boxin, Yang, Zhuoling, Liu, Zihan, Barker, Jon, Rintamaki, Tuomas, Shoeybi, Mohammad, Catanzaro, Bryan, Ping, Wei
We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks, rivaling the leading proprietary models (e.g., GPT-4o) and open-access models (e.g., Llama 3-V 4
Externí odkaz:
http://arxiv.org/abs/2409.11402
Autor:
Dabike, Gerardo Roa, Akeroyd, Michael A., Bannister, Scott, Barker, Jon P., Cox, Trevor J., Fazenda, Bruno, Firth, Jennifer, Graetzer, Simone, Greasley, Alinka, Vos, Rebecca R., Whitmer, William M.
It is well established that listening to music is an issue for those with hearing loss, and hearing aids are not a universal solution. How can machine learning be used to address this? This paper details the first application of the open challenge me
Externí odkaz:
http://arxiv.org/abs/2409.05095
Machine learning techniques are an active area of research for speech enhancement for hearing aids, with one particular focus on improving the intelligibility of a noisy speech signal. Recent work has shown that feature encodings from self-supervised
Externí odkaz:
http://arxiv.org/abs/2407.13333
Autor:
Leglaive, Simon, Fraticelli, Matthieu, ElGhazaly, Hend, Borne, Léonie, Sadeghi, Mostafa, Wisdom, Scott, Pariente, Manuel, Hershey, John R., Pressnitzer, Daniel, Barker, Jon P.
Supervised models for speech enhancement are trained using artificially generated mixtures of clean speech and noise signals. However, the synthetic training conditions may not accurately reflect real-world conditions encountered during testing. This
Externí odkaz:
http://arxiv.org/abs/2402.01413
Autor:
Mogridge, Rhiannon, Close, George, Sutherland, Robert, Hain, Thomas, Barker, Jon, Goetze, Stefan, Ragni, Anton
Neural networks have been successfully used for non-intrusive speech intelligibility prediction. Recently, the use of feature representations sourced from intermediate layers of pre-trained self-supervised and weakly-supervised models has been found
Externí odkaz:
http://arxiv.org/abs/2401.13611
Autor:
Cox, Trevor J., Barker, Jon, Bailey, Will, Graetzer, Simone, Akeroyd, Michael A., Culling, John F., Naylor, Graham
This paper reports on the design and outcomes of the ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids. The scenario was a listener attending to a target speaker in a noisy, domestic environment. There were multiple interferers and hea
Externí odkaz:
http://arxiv.org/abs/2311.14490
This paper describes two intelligibility prediction systems derived from a pretrained noise-robust automatic speech recognition (ASR) model for the second Clarity Prediction Challenge (CPC2). One system is intrusive and leverages the hidden represent
Externí odkaz:
http://arxiv.org/abs/2310.19817
Autor:
Dabike, Gerardo Roa, Bannister, Scott, Firth, Jennifer, Graetzer, Simone, Vos, Rebecca, Akeroyd, Michael A., Barker, Jon, Cox, Trevor J., Fazenda, Bruno, Greasley, Alinka, Whitmer, William
The Cadenza project aims to improve the audio quality of music for those who have a hearing loss. This is being done through a series of signal processing challenges, to foster better and more inclusive technologies. In the first round, two common li
Externí odkaz:
http://arxiv.org/abs/2310.05799
Autor:
Dabike, Gerardo Roa, Akeroyd, Michael A., Bannister, Scott, Barker, Jon, Cox, Trevor J., Fazenda, Bruno, Firth, Jennifer, Graetzer, Simone, Greasley, Alinka, Vos, Rebecca R., Whitmer, William M.
This paper reports on the design and results of the 2024 ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The Cadenza project is working to enhance the audio quality of music for those with a hearing loss. The scenario for the c
Externí odkaz:
http://arxiv.org/abs/2310.03480