Accurate animal sound classification is an important task in automated animal monitoring system. Such monitoring system is essential for preventing epidemics caused by animal disease. Based on such needs, there has been a variety of efforts to develop an accurate system performing animal sound classification in deep learning framework. Although many research issues and methods to address the issues were introduced, no one has yet to address overcoming the machine learning barriers induced by a single objective function. As learnable parameters only consider a single penalty at the output prediction for training, they cannot capture other characteristics contained in the dataset to extract more generalized prediction. This paper proposes a deep learning based multi-task learning framework for animal sound classification. Both animal species and group classification are performed in an end-to-end learning process. Experimental results show that the proposed multi-task method outperforms single-task method in our recorded animal sound dataset.
|Title of host publication||ICIT 2019 - Proceedings of the 7th International Conference on Information Technology|
|Subtitle of host publication||IoT and Smart City|
|Publisher||Association for Computing Machinery|
|Number of pages||4|
|Publication status||Published - 2019 Dec 20|
|Event||7th International Conference on Information Technology: IoT and Smart City, ICIT 2019 - Shanghai, China|
Duration: 2019 Dec 20 → 2019 Dec 23
|Name||ACM International Conference Proceeding Series|
|Conference||7th International Conference on Information Technology: IoT and Smart City, ICIT 2019|
|Period||19/12/20 → 19/12/23|
Bibliographical noteFunding Information:
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Public Technology Program based on Environmental Policy, funded by Korea Ministry of Environment (MOE) (2017000210001).
© 2019 Association for Computing Machinery.
- Animal group
- Animal species
- Deep learning
- Multi-task learning
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications