Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification
AU - Xu, Weitao
AU - Zhang, Xiang
AU - Yao, Lina
AU - Xue, Wanli
AU - Wei, Bo
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer fromseveral disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based oncloud architecture which relaxes the computational burden on the wireless sensor node. To improve therecognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that theproposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implementand deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of theproposed acoustic classification system in distinguishing species of animals.
AB - Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer fromseveral disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based oncloud architecture which relaxes the computational burden on the wireless sensor node. To improve therecognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that theproposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implementand deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of theproposed acoustic classification system in distinguishing species of animals.
KW - Wireless acoustic sensor network
KW - Animal identification
KW - Deep learning
KW - CNN
U2 - 10.1016/j.adhoc.2020.102115
DO - 10.1016/j.adhoc.2020.102115
M3 - Journal article
VL - 102
JO - Ad Hoc Networks
JF - Ad Hoc Networks
SN - 1570-8705
M1 - 102115
ER -