文章基本信息

标题：Acoustic DOA estimation using space alternating sparse Bayesian learning
本地全文：下载
作者：Zonglong Bai ; Liming Shi ; Jesper Rindom Jensen 等
期刊名称：EURASIP Journal on Audio, Speech, and Music Processing
印刷版ISSN：1687-4714
电子版ISSN：1687-4722
出版年度：2021
卷号：2021
期号：1
页码：1
DOI：10.1186/s13636-021-00200-z
出版社：Hindawi Publishing Corporation
摘要：Estimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoid robots and drones. However, it is a most challenging problem due to a number of factors, including the platform size which puts a constraint on the array aperture. To overcome this problem, a high-resolution DOA estimation algorithm based on sparse Bayesian learning is proposed in this paper. A group sparse prior based hierarchical Bayesian model is introduced to encourage spatial sparsity of acoustic sources. To obtain approximate posteriors of the hidden variables, a variational Bayesian approach is proposed. Moreover, to reduce the computational complexity, the space alternating approach is applied to push the variational Bayesian inference to the scalar level. Furthermore, an acoustic DOA estimator is proposed to jointly utilize the estimated source signals from all frequency bins. Compared to state-of-the-art approaches, the high-resolution performance of the proposed approach is demonstrated in experiments with both synthetic and real data. The experiments show that the proposed approach achieves lower root mean square error (RMSE), false alert (FA), and miss-detection (MD) than other methods. Therefore, the proposed approach can be applied to some applications such as humanoid robots and drones to improve the resolution performance for acoustic DOA estimation especially when the size of the array aperture is constrained by the platform, preventing the use of traditional methods to resolve multiple sources.
关键词：Sparse Bayesian learning ; Acoustic DOA estimation ; Sound source localization