## 1 Introduction  
Bird species have been encountering increasing threats in recent years, which has attracted wide attention from ornithologists [1]. It is significant to monitor bird species for bird protection. Withwireless acoustic sensor networks (WASN) becoming more and more popular, WASNs are commonly used for monitoring bird audio long-term [2]. Automatic bird species identification provides a suitable way to analyze the huge audio data from long-term monitoring programs [3]. However, the bird audio collected in nature is always accompanied by ambient noises, which consequently affect the accuracy of the bird species identification [4]. Therefore, audio enhancement should be carried out before identification, to improve identification accuracy. By exploiting the spatial properties of speech and noise signals, WASN techniques can significantly outperform single-channel techniques in terms of improving interference suppression and reducing speech distortion [5; 6; 7; 8; 9]. Although WASN has many advantages, it also has several challenges, such as the limited energy and calculation ability of each node. There are two kinds of audio enhancement algorithms for WASNs. The first is centralized, all the data is transferred to a so-called fusion center (FC) for further enhancement. The second is distributed, the enhancements are performed on all the nodes, which means the computations are decomposed to all the nodes, then the amount of data transferred between nodes is reduced. Compared with the centralized method, the distributed method is typically preferred because of its lower energy consumption and higher scalability [10]. Several studies have been conducted on distributed methods of speech enhancement in WASNs. Three distributed audio enhancement algorithms [11; 12; 13] were presented in full-connect or tree topology networks, which can achieve the optimal estimated beamformer results at each node by using data compression to reduce energy consumption. [PERSON] and [PERSON] proposed a distributed delay and sum beamformer (DDSB) for speech enhancement via randomized gossip in any topology of WASN--the same result as centralized beamformers can be obtained in each node of WASN [14]. Moreover, on the basis of previous studies, an improved general distributed synchronous averaging (IGDSA) algorithm was proposed [15] and the proposed IGDSA algorithm presented faster coverage rate than original synchronous communication scheme, especially for non-regular networks. [PERSON] proposed a speech enhancement algorithm through a distributed minimum variance distortionless response (DMVDR) beamformer. With this algorithm, each node in the WASN can obtain the same estimation result by communicating with neighboring nodes [16]. For any network topology, the distributed beamformer can estimate the desired signal at each node by only exchanging information with its neighbors. Only local signal interchanges are implemented in the distributed beamformer, which brings the advantages of higher robustness and scalability for sensor networks with a large node number and dynamic topology. So, the distributed beamformer is chosen for bird audio enhancement in this paper. Despite the rapid development of the speech enhancement technology of WASNs, research on the enhancement of bird audio using WASNs in forest areas is scarce. The noises in forest areas are always time-variant. However, in the traditional distributed speech enhancement of WASNs, the noise power spectral density (PSD) is commonly estimated by means of temporal averaging over noise-only segments [11; 12; 13; 14; 15; 16], which may introduce large residual noise [17]. Moreover, the convergence rates of existing distributed beamformer methods are not fast enough, such as the random gossip algorithm of DDSB [14; 15]. These restrictions obstruct the practical utilization of the distributed beamformer method.