Alam Faisal, Usman Mohammed, Alkhammash Hend I, Wajid Mohd
Department of Computer Engineering, Z.H.C.E.T., Aligarh Muslim University, Aligarh 202002, India.
Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia.
Sensors (Basel). 2021 Apr 11;21(8):2692. doi: 10.3390/s21082692.
The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.
利用诸如广义互相关、波束形成、子空间技术等经典技术，可通过均匀线性阵列估计声源的到达方向（DoA）。然而，这些方法需要在角度空间中进行搜索，并且在端射时具有较高的角度误差。在本文中，我们提出使用回归技术来改善包括端射在内的所有角度的DoA估计结果。所提出的方法对接收到的多通道麦克风信号进行曲线拟合，与传统技术和其他多项式回归技术相比，当与支持向量回归（SVR）串联应用时，能更好地估计DoA。还提出了一种多级回归技术，该技术进一步提高了端射时的估计精度。这种多级回归技术对从SVR获得的结果进行线性回归。与经典广义互相关技术相比，这里采用的技术总体上提高了63%。
