2021/07/24 by Yunpeng Li, YunPeng Li, Li, YunPeng
Chemistry · Computer Science · Engineering · #Blind Source Separation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Spectroscopy and Chemometric Analyses #Speech and Audio Processing #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2107.14135
arxiv created 2021/07/24 · openalex publication_date 2021/07/24 · arxiv updated 2021/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Convolutive blind source separation (BSS) is intended to recover the unknown components from their convolutive mixtures. Contrary to the contrast functions used in instantaneous cases, the spatial-temporal prewhitening stage and the para-unitary filters constraint are difficult to implement in a convolutive context. In this paper, we propose several modifications of FastICA to alleviate these difficulties. Our method performs the simple prewhitening step on convolutive mixtures prior to the separation and optimizes the contrast function under the diagonalization constraint implemented by single value decomposition (SVD). Numerical simulations are implemented to verify the performance of the proposed method.