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A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Weitao Xu
  • Xiang Zhang
  • Lina Yao
  • Wanli Xue
  • Bo Wei
Article number102115
<mark>Journal publication date</mark>1/05/2020
<mark>Journal</mark>Ad Hoc Networks
Number of pages11
Publication StatusPublished
Early online date8/03/20
<mark>Original language</mark>English


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 from
several 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 on
cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the
recognition 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 the
proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement
and 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 the
proposed acoustic classification system in distinguishing species of animals.