4.7 Article

An Efficient Time-Domain End-to-End Single-Channel Bird Sound Separation Network

期刊

ANIMALS
卷 12, 期 22, 页码 -

出版社

MDPI
DOI: 10.3390/ani12223117

关键词

bird sound separation; transformer; deep learning; lower computational resources; dual-path network

资金

  1. National Natural Science Foundation of China [32171520]
  2. Research Project of the Education Bureau of Guangzhou [202032882]

向作者/读者索取更多资源

This study proposes an efficient time-domain single-channel bird sound separation network that achieves good separation performance and high separation efficiency. By utilizing massive amounts of bird sound data and incorporating a dual-path network and simplified transformer structure, the network significantly reduces computational resources while maintaining good separation results. The proposed network has the potential to contribute to distinguishing individual birds, studying bird interactions, and enabling automatic identification of bird species on various mobile and edge computing devices.
Simple Summary Automatic bird sound recognition using artificial intelligence technology has been widely used to identify bird species recently. However, the bird sounds recorded in the wild are usually mixed sounds, which can affect the accuracy of identification. In this paper, we utilized massive amounts of data of bird sounds and proposed an efficient time-domain single-channel bird sound separation network. Our proposed network achieved good separation performance and fast separation speed while greatly reducing the consumption of computational resources. Our work may help to discriminate individual birds and study the interaction between individual birds, as well as to realize the automatic identification of bird species in various mobile and edge computing devices. Bird sounds have obvious characteristics per species, and they are an important way for birds to communicate and transmit information. However, the recorded bird sounds in the field are usually mixed, which making it challenging to identify different bird species and to perform associated tasks. In this study, based on the supervised learning framework, we propose a bird sound separation network, a dual-path tiny transformer network, to directly perform end-to-end mixed species bird sound separation in the time-domain. This separation network is mainly composed of the dual-path network and the simplified transformer structure, which greatly reduces the computational resources required of the network. Experimental results show that our proposed separation network has good separation performance (SI-SNRi reaches 19.3 dB and SDRi reaches 20.1 dB), but compared with DPRNN and DPTNet, its parameters and floating point operations are greatly reduced, which means a higher separation efficiency and faster separation speed. The good separation performance and high separation efficiency indicate that our proposed separation network is valuable for distinguishing individual birds and studying the interaction between individual birds, as well as for realizing the automatic identification of bird species on a variety of mobile devices or edge computing devices.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据