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

Abstract

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.

Sound Localization and Speech Enhancement Algorithm Based on Dual-Microphone

“…On the other hand, the acoustic signal can also be seen as a spherical wave. After vibration occurs at the acoustic source to generate an acoustic signal, the medium near the acoustic source appears accompanied by vibration, and the voice signal spreads around along with the medium simultaneously [ 17 ].…”

Performance evaluation of direction-finding techniques of an acoustic source with uniform linear array

“…It minimizes the parameter, min12||boldw||2 , where w is the vector normal to the tube. The emphasis is on finding the most flattened tube such that most of the predictions lie within its boundaries (Awad and Khanna, 2015; Alam et al , 2021; Drucker et al , 1997).…”

“…DAS algorithm is a beamforming technique that estimates DOA using signal power, bold-italicPDAS(θ). The DOA is estimated by searching for the values of θ for which bold-italicPDAS(θ) show peaks (Awad and Khanna, 2015; Alam et al , 2021; Drucker et al , 1997). bold-italicPDAS(θ) is defined as follows:where a̲(θ) contains the look-angle vector of ULA.…”

“…The presence of distortion causes inaccurate estimation of DOAs of an acoustic source. Techniques for DOA estimation with small errors in the presence of impairments use different acoustic vector sensors and microphone/sensor array configurations (Bogaert et al , 2011; Wajid et al , 2017a, b, 2019, 2020a, b; Yadav et al , 2020; Alam et al , 2021; Liu et al , 2019). The DOA estimation techniques are classified as based on regression, beamforming and subspace method.…”