宽带声源方位估计的多频稀疏贝叶斯学习改进算法

Improved multi-frequency sparse Bayesian learning method for DOA estimation of the wideband sound source

在对空气中未知的宽带声源的波达方向进行估计时,麦克风阵列的阵元间距很容易大于声信号半波长而出现栅瓣,严重影响估计效果。尽管多频带数据的使用在一定程度上可以抑制栅瓣产生,但目前的方法抑制效果比较一般而且计算效率不高。在稀疏贝叶斯学习基础上,提出了一种针对宽带声源方位估计的改进方法。这种方法将超先验引入到传统的多频稀疏贝叶斯估计模型中,然后同时利用声源信号在多个频带上具有的相同空间角度稀疏性并结合期望最大化算法重新推导了多频稀疏贝叶斯模型中各相关参数的迭代形式。与此同时,考虑到实际场景中的声源方位通常不位于稀疏网格上,离网格修复模型也被加入设计框架中,以解决该问题。为验证算法性能,开展了仿真实验和场地实验。结果表明,相比最近提出的基于l_(1)最小化的多频压缩感知方法和宽带的多频稀疏贝叶斯学习方法,提出方法能更好的利用宽带声源的多频特性以降低栅瓣的干扰,同时具有更高的估计精度和计算速度。在现场实验中,改进方法表现了优于其他先进方法的栅瓣抑制能力,声源方位估计误差可达0.09°,所需迭代收敛步数相比MF-SBL减少约50%。

The grating lobe appears when the microphone array element spacing is larger than the half-wavelength of the acoustic signal for the DOA estimation of the wideband sound source.Although the utilization of multi-frequency bins data can suppress the grating lobes to some degree,the current methods perform unsatisfactorily and are computationally inefficient.To address these issues,an improved method based on the sparse Bayesian learning is proposed for wideband DOA estimation.This method introduces the hyperprior to the multi-frequency sparse Bayesian estimation model,and then takes advantage of the fact that the source signal has the same sparsity in multi-frequency bins and combines the expectation maximization algorithm to derive the iterative form of each hyperparameter in the multi-frequency sparse Bayesian model.In addition,an off-grid model for the wideband sound source is incorporated into the proposed framework to better fit the practical scenarios.To evaluate the performance of the algorithm,simulations and field experiments are implemented.Results show that the proposed method can better exploit the multi-frequency characteristics of the wideband sound source to reduce the interference of the grating lobes,while having higher estimation accuracy and faster estimation speed compared with the multi-frequency compressive sensing method with l_(1)minimization and the multi-frequency sparse Bayesian learning method.In the practical tests,the improved method shows better grating lobe suppression ability than other advanced methods,and its estimation error can reach 0.09°.Compared with MF-SBL,the number of iterative convergence steps required is reduced by about 50%.