no code implementations • 13 Mar 2023 • Tong Xia, Jing Han, Abhirup Ghosh, Cecilia Mascolo
In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics.
no code implementations • 23 Apr 2022 • Yang Zhao, Kai Zhang, Haotian Yu, Yi Zhang, Dongliang Zheng, Jing Han
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics.
no code implementations • 17 Feb 2022 • Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton Batliner, Cecilia Mascolo, Björn W. Schuller
The COVID-19 pandemic has caused massive humanitarian and economic damage.
no code implementations • 4 Jan 2022 • Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
no code implementations • 16 Dec 2021 • Tong Xia, Jing Han, Cecilia Mascolo
A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring.
no code implementations • 10 Dec 2021 • Qiming Ye, Yuxiang Feng, Jing Han, Marc Stettler, Panagiotis Angeloudis
An intelligent street can learn and improve its decision-making on the right-of-way (ROW) for road users, liberating more active pedestrian space while maintaining traffic safety and efficiency.
no code implementations • 6 Sep 2021 • Hui Xie, Zhuang Zhao, Jing Han, Yi Zhang, Lianfa Bai, Jun Lu
Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs.
no code implementations • 9 Aug 2021 • Erika Bondareva, Jing Han, William Bradlow, Cecilia Mascolo
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination.
no code implementations • 29 Jun 2021 • Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Ting Dang, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
no code implementations • 19 Apr 2021 • Shuo Liu, Jing Han, Estela Laporta Puyal, Spyridon Kontaxis, Shaoxiong Sun, Patrick Locatelli, Judith Dineley, Florian B. Pokorny, Gloria Dalla Costa, Letizia Leocan, Ana Isabel Guerrero, Carlos Nos, Ana Zabalza, Per Soelberg Sørensen, Mathias Buron, Melinda Magyari, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Amos A Folarin, Richard JB Dobson, Raquel Bailón, Srinivasan Vairavan, NIcholas Cummins, Vaibhav A Narayan, Matthew Hotopf, Giancarlo Comi, Björn Schuller
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
no code implementations • 5 Apr 2021 • Tong Xia, Jing Han, Lorena Qendro, Ting Dang, Cecilia Mascolo
To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.
no code implementations • 24 Feb 2021 • Björn W. Schuller, Anton Batliner, Christian Bergler, Cecilia Mascolo, Jing Han, Iulia Lefter, Heysem Kaya, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Maurice Gerczuk, Panagiotis Tzirakis, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
no code implementations • 28 Dec 2020 • Xiaoyu Chen, Chi Zhang, Guosheng Lin, Jing Han
Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
no code implementations • 9 Aug 2020 • Jiang Yue, Weisong Wen, Jing Han, Li-Ta Hsu
Inspired by the complementariness of 3D LiDAR and camera, this paper proposes to make use of the high-resolution images from a camera to enrich the raw 3D point clouds from the low-cost 16 channels LiDAR based on a state-of-the-art deep learning algorithm.
4 code implementations • 10 Jun 2020 • Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Cecilia Mascolo
This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
no code implementations • 30 Apr 2020 • Jing Han, Kun Qian, Meishu Song, Zijiang Yang, Zhao Ren, Shuo Liu, Juan Liu, Huaiyuan Zheng, Wei Ji, Tomoya Koike, Xiao Li, Zixing Zhang, Yoshiharu Yamamoto, Björn W. Schuller
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
no code implementations • 20 Oct 2019 • Enlai Guo, Shuo Zhu, Yan Sun, Lianfa Bai, Jing Han
Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields.
no code implementations • 23 Jul 2019 • Jing Han, Zixing Zhang, Zhao Ren, Björn Schuller
Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand.
no code implementations • 29 Jun 2019 • XiaoYu Chen, Xu Wang, Lianfa Bai, Jing Han, Zhuang Zhao
In this paper, we present a convolution neural network based method to recover the light intensity distribution from the overlapped dispersive spectra instead of adding an extra light path to capture it directly for the first time.
no code implementations • 29 Jun 2019 • Xiaoyu Chen, Qixin Wang, Jinzhou Ge, Yi Zhang, Jing Han
At present, supervised stereo methods based on deep neural network have achieved impressive results.
no code implementations • 29 Mar 2019 • Zixing Zhang, Jing Han, Kun Qian, Christoph Janott, Yanan Guo, Bjoern Schuller
One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data.
1 code implementation • 23 Mar 2019 • Xiaoyu Chen, Xiaotian Lou, Lianfa Bai, Jing Han
In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentation by decomposing the label at different levels of residual blocks.
no code implementations • 9 Jan 2019 • Jean Kossaifi, Robert Walecki, Yannis Panagakis, Jie Shen, Maximilian Schmitt, Fabien Ringeval, Jing Han, Vedhas Pandit, Antoine Toisoul, Bjorn Schuller, Kam Star, Elnar Hajiyev, Maja Pantic
Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life.
no code implementations • 26 Oct 2018 • Gil Keren, Jing Han, Björn Schuller
We address the problem of speech enhancement generalisation to unseen environments by performing two manipulations.
no code implementations • 21 Sep 2018 • Jing Han, Zixing Zhang, NIcholas Cummins, Björn Schuller
Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.
no code implementations • 27 Jul 2017 • Zixing Zhang, Ding Liu, Jing Han, Kun Qian, Björn Schuller
Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.
no code implementations • 12 May 2017 • Yahui Liu, Jian Yao, Li Li, Xiaohu Lu, Jing Han
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network.