Computational Modelling Auditory Awareness

Yu Su, Jingyu Wang, Ke Zhang, Kurosh Madani, Xianyu Wang

Abstract

Research in the human voice and environment sound recognition has been well studied during the past decades. Nowadays, modeling auditory awareness has received more and more attention. Its basic concept is to imitate the human auditory system to give artificial intelligence the auditory perception ability. In order to successfully mimic human auditory mechanism, several models have been proposed in the past decades. In view of deep learning (DL) algorithms has better classification performance than conventional approaches (such as GMM and HMM), the latest research works mainly focused on building auditory awareness models based on deep architectures. In this survey, we will offer a quality and compendious survey on recent auditory awareness models and development trend. This article includes three parts: i) classical auditory saliency detection method and developments during the past decades, ii) the application of machine learning in ASD. Finally, summarizing comments and development trends in this filed will be given.

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Paper Citation


in Harvard Style

Su Y., Wang J., Zhang K., Madani K. and Wang X. (2018). Computational Modelling Auditory Awareness.In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-327-8, pages 160-167. DOI: 10.5220/0006925401600167


in Bibtex Style

@conference{ijcci18,
author={Yu Su and Jingyu Wang and Ke Zhang and Kurosh Madani and Xianyu Wang},
title={Computational Modelling Auditory Awareness},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2018},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006925401600167},
isbn={978-989-758-327-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Computational Modelling Auditory Awareness
SN - 978-989-758-327-8
AU - Su Y.
AU - Wang J.
AU - Zhang K.
AU - Madani K.
AU - Wang X.
PY - 2018
SP - 160
EP - 167
DO - 10.5220/0006925401600167