Affiliations: [a] School of Energy and Power Engineering, University of Shanghai For Science and Technology, Shanghai, China
| [b] School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, China
| [c]
Qingdao Technological University, Key Lab of Industrial Fluid Energy Conservation and Pollution Control, Qingdao, China
| [d] Department of Financial Engineering, Nation University, USA
Correspondence:
[*]
Corresponding author. Daolai Cheng, School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai, 200093, China. E-mail: btrx5832@sina.com and Sandra Zick, Department of Financial Engineering, Nation University, USA. E-mail: aqkkkaqi@pipeline.sbcc.edu.
Abstract: At present, the CAAC has established the only “Aircraft Cabin Sound Information Sample Library” in China, which provides strong support for the theoretical analysis method based on the CVR non-discourse sound blind source separation. The separation of aircraft background acoustic blindness based on EEMD-ICA is studied. The performance of different algorithms for the separation of CVR non-discourse background acoustic typical observation signals is compared, and an incompletely constrained adaptive natural gradient algorithm is found for signals that change drastically over time and have a near-zero amplitude over a more extended period. In addition, when there is redundant information or noise on the CVR background acoustic signal, an independent component analysis method is used to reduce the dimensionality of the observed signal, which is essential for extracting valuable information from confounded signals and provides a reference for dealing with changing mixed signals.
