ArticleSensors (Basel, Switzerland)2021
Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Subspace Complexity Reduction in Direction-of-Arrival Estimation via the RASA Algorithm.Sensors (Basel, Switzerland) · 2025Article
- A Reconfigurable Digital Beamformer Implemented on a Field-Programmable Gate Array for Real-Time and Resource-Efficient Direction-of-Arrival Estimation.Sensors (Basel, Switzerland) · 2025Article
- Sound Localization and Speech Enhancement Algorithm Based on Dual-Microphone.Sensors (Basel, Switzerland) · 2022Article
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4 authors.
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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.
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