Canonical correlation analysis using for DOA estimation of multiple audio sources

Authors:
Gaoming Huang;Luxi Yang;Zhenya He
Affiliations:
Naval University of Engineering, Wuhan, China;Department of Radio Engineering, Southeast University, Nanjing, China;Department of Radio Engineering, Southeast University, Nanjing, China
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 2
Cited 1

Kernel independent component analysis

The Journal of Machine Learning Research
Blind separation of delayed sources based on information maximization

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06

Application of blind source separation to five-element cross array passive location

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we study direction of arrival (DOA) estimation of multiple audio sources by canonical correlation analysis (CCA), which is based on a sparse linear arrays. This array is composed of two separated subarrays. From the receiving data set, we can obtain the separate components by CCA. After a simple correlation, time difference can be obtained, and then we can compute the azimuth of different audio sources. The important contribution of this new estimation method is that it can reduce the effect of inter-sensor spacing to DOA estimation and the computation burden is light. Simulation result confirms the validity and practicality of the proposed approach. Results of DOA estimation are more accurate and stable based on this new method.