Sound event localization and classification using WASN in Outdoor
  Environment
Dongzhe Zhang Jianfeng Chen Jisheng Bai Mou Wang
Deep learning-based sound event localization and classification is an
emerging research area within wireless acoustic sensor networks. However,
current methods for sound event localization and classification typically rely
on a single microphone array, making them susceptible to signal attenuation and
environmental noise, which limits their monitoring range. Moreover, methods
using multiple microphone arrays often focus solely on source localization,
neglecting the aspect of sound event classification. In this paper, we propose
a deep learning-based method that employs multiple features and attention
mechanisms to estimate the location and class of sound source. We introduce a
Soundmap feature to capture spatial information across multiple frequency
bands. We also use the Gammatone filter to generate acoustic features more
suitable for outdoor environments. Furthermore, we integrate attention
mechanisms to learn channel-wise relationships and temporal dependencies within
the acoustic features. To evaluate our proposed method, we conduct experiments
using simulated datasets with different levels of noise and size of monitoring
areas, as well as different arrays and source positions. The experimental
results demonstrate the superiority of our proposed method over
state-of-the-art methods in both sound event classification and sound source
localization tasks. And we provide further analysis to explain the reasons for
the observed errors.