Binti Abdullah, S;
Demosthenous, A;
Yasin, I;
(2020)
Comparison of Auditory-Inspired Models Using Machine-Learning for Noise Classification.
In:
International Journal of Simulation Systems, Science & Technology Special Issue: Conference Procedings UKSim2020, 25 to 27 March 2020.
(pp. 20.1-20.9).
United Kingdom Simulation Society: Cambridge, UK.
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Abstract
Two auditory-inspired feature-extraction models, the Multi-Resolution CochleaGram (MRCG) and the Auditory Image Model (AIM) are compared on their acoustic noise classification performance, when combined with two supervised machine-learning algorithms, the ensemble bagged of decision trees or Support Vector Machine (SVM). Noise classification accuracies are then assessed in nine different sound environments with or without added speech and at different SNR ratios. The results demonstrate that classification scores using feature extraction with the MRCG model are significantly higher than when using the AIM model (p< 0.05), irrespective of machine-learning classifier. Using the SVM as a classifier also resulted in significantly better (p<0.05) classification performance over bagged trees, irrespective of feature-extraction model. Overall, the MRCG model combined with SVM provides a more accurate classification for most of the sound stimuli tested. From the comparison study, suggestions on how auditory model-plus-machine-learning can be improved for the purpose of sound classification are offered.
| Type: | Proceedings paper |
|---|---|
| Title: | Comparison of Auditory-Inspired Models Using Machine-Learning for Noise Classification |
| Event: | UKSim-AMSS 22nd International Conference on Modelling & Simulation (UKSim2020) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5013/IJSSST.a.21.02.20 |
| Publisher version: | https://ijssst.info/Vol-21/No-2/paper20.pdf |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10095070 |
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