Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation

Sensors

Faisal Alam¹  HendI. Alkhammash²  Mohammed Usman³  Mohd Wajid⁴ 

[1] Department of Computer Engineering, Z.H.C.E.T., Aligarh Muslim University, Aligarh 202002, India;Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia;Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia;Department of Electronics Engineering, Z.H.C.E.T., Aligarh Muslim University, Aligarh 202002, India;

关键词: correlation coefficient;    curve fitting;    direction-of-arrival estimation;    machine learning;    microphone array;    support vector regression;   

DOI  :  10.3390/s21082692

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown