Open AccessApplication of the denoising technique based on improved FastICA algorithm in cross-correlation time delay estimation
Author(s) -
Junhe Shi,
Hua Yan,
Yuankun Wei
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012196
Subject(s) - fastica , algorithm , noise (video) , computer science , noise reduction , gradient descent , sensitivity (control systems) , correlation , mathematics , artificial intelligence , artificial neural network , blind signal separation , engineering , telecommunications , electronic engineering , channel (broadcasting) , geometry , image (mathematics)
Cross-correlation is a common time delay estimation method. However, in the actual measurement environment, the near-end and far-end sensors are often disturbed by the correlated noise, which seriously affects the accuracy of time delay estimation and even leads algorithm is proposed, and the steepest descent is introduced to reduce the sensitivity of FastICA algorithm to initial values. The useful acoustic data and disturbing acoustic data were measured respectively in a simulated granary. From these data, the signals contaminated by noise were generated by a mixing matrix and used for testing the performance of the proposed method. The testing results show that the proposed method can effectively suppress the influence of correlated noise on cross-correlation time delay estimation, and improve the accuracy of estimation.