Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Niesler, Thomas"'
We consider the problem of detecting, isolating and classifying elephant calls in continuously recorded audio. Such automatic call characterisation can assist conservation efforts and inform environmental management strategies. In contrast to previou
Externí odkaz:
http://arxiv.org/abs/2410.12082
Annotating a multilingual code-switched corpus is a painstaking process requiring specialist linguistic expertise. This is partly due to the large number of language combinations that may appear within and across utterances, which might require sever
Externí odkaz:
http://arxiv.org/abs/2312.09645
In this work, we explore recurrent neural network architectures for tuberculosis (TB) cough classification. In contrast to previous unsuccessful attempts to implement deep architectures in this domain, we show that a basic bidirectional long short-te
Externí odkaz:
http://arxiv.org/abs/2209.00934
Autor:
Pahar, Madhurananda, Klopper, Marisa, Reeve, Byron, Warren, Rob, Theron, Grant, Diacon, Andreas, Niesler, Thomas
Publikováno v:
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022, pp. 1-9
We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough as a predominant symptom and cl
Externí odkaz:
http://arxiv.org/abs/2205.05480
Publikováno v:
IEEE Access, vol. 11, pp. 30739-30752, 2023
We present a new machine learning based bed-occupancy detection system that uses the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed-occupancy detection is necessary for automatic long-term cough monitoring, since t
Externí odkaz:
http://arxiv.org/abs/2202.03936
Documents containing mathematical content remain largely inaccessible to blind and visually impaired readers because they are predominantly published as untagged PDF which does not include the semantic data necessary for effective accessibility. We p
Externí odkaz:
http://arxiv.org/abs/2202.01639
Autor:
Pahar, Madhurananda, Klopper, Marisa, Reeve, Byron, Warren, Rob, Theron, Grant, Diacon, Andreas, Niesler, Thomas
We present `wake-cough', an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system. Coughs, recorded in a quiet (73$\pm$5 d
Externí odkaz:
http://arxiv.org/abs/2110.03771
Publikováno v:
Journal of Signal Processing Systems, 2022
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audi
Externí odkaz:
http://arxiv.org/abs/2109.00103
We consider feature learning for efficient keyword spotting that can be applied in severely under-resourced settings. The objective is to support humanitarian relief programmes by the United Nations in parts of Africa in which almost no language reso
Externí odkaz:
http://arxiv.org/abs/2108.06174
We present first speech recognition systems for the two severely under-resourced Malian languages Bambara and Maasina Fulfulde. These systems will be used by the United Nations as part of a monitoring system to inform and support humanitarian program
Externí odkaz:
http://arxiv.org/abs/2108.06164