Improved Distributed Minimum Variance Distortionless Response (MVDR) Beamforming Method Based on a Local Average Consensus Algorithm for Bird Audio Enhancement in Wireless Acoustic Sensor Networks

Abstract

Featured Application: With this bird audio enhancement method, the bird audio collected through the WASN (Wireless Acoustic Sensor Network) can be processed to produce better quality audio, which is more suitable for bird species identification based on the bird audio, then a higher accuracy of identification will be achieved. Abstract: Currently, wireless acoustic sensor networks (WASN) are commonly used for wild bird monitoring. To better realize the automatic identification of birds during monitoring, the enhancement of bird audio is essential in nature. Currently, distributed beamformer is the most suitable method for bird audio enhancement of WASN. However, there are still several disadvantages of this method, such as large noise residue and slow convergence rate. To overcome these shortcomings, an improved distributed minimum variance distortionless response (IDMVDR) beamforming method for bird audio enhancement in WASN is proposed in this paper. In this method, the average metropolis weight local average consensus algorithm is first introduced to increase the consensus convergence rate, then a continuous spectrum update algorithm is proposed to estimate the noise power spectral density (PSD) to improve the noise reduction performance. Lastly, an MVDR beamformer is introduced to enhance the bird audio. Four different network topologies of the WASNs were considered, and the bird audio enhancement was performed on these WASNs to validate the effectiveness of the proposed method. Compared with two classical methods, the results show that the Segmental signal to noise ratio (SegSNR), mean square error (MSE), and perceptual evaluation of speech quality (PESQ) obtained by the proposed method are better and the consensus rate is faster, which means that the proposed method performs better in audio quality and convergence rate, and therefore it is suitable for WASN with dynamic topology. wireless 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 ...

Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation

“…In [26], for improvement of audio enhancement, a new improved distributed minimum variance distortionless response (MVDR) beamforming algorithm is proposed. The algorithm can be applied to wireless acoustic sensor networks.…”

“…In Table 1, algorithms, applications, main ideas and contributions of the recently published works are tabulated. In Table 2, it is tabulated how the schemes presented in [17][18][19][20][21][22][23][24][25][26] are validated. In Table 2, it is also described how the explicit expressions of the azimuth estimate and the MSE of the azimuth estimation error derived in this paper can be applied to the problems in [17][18][19][20][21][22][23][24][25][26].…”

“…How to validate the schemes presented in [17][18][19][20][21][22][23][24][25][26], and how to apply the derivations of this paper to the problems in [17][18][19][20][21][22][23][24][25][26]. [17][18][19][20][21][22][23][24][25][26] to the Problems in [17][18][19][20][21][22][23][24][25][26] [17] Agreement between analytic MSE [17]: Performance analysis of amplitude and simulation-based MSE comparison monopulse algorithm. This paper: Performance analysis of the MUSIC algorithm [18] Reduction in estimation error of the The analytic expression of the MSE of motion compensated conventional motion-compensated Bartlett beamforming beamforming algorithm in comparison with algorithm can be obtained by modifying the estimation error of original the derivation in this paper conventional beamforming algorithm without motion compensation in the DOA spectrum [19] Superiority of performance of SCC-The analytic expression of the MSE of MVDR algorithm over MVDR algorithm SCC-MVDR algorithm can be o...…”