Variable learning rate EASI-based adaptive blind source separation in situation of nonstationary source and linear time-varying systems
| Autor: | Cheng Wang, Chen Yewang, Yi-wen Zhang, Haiyang Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
nonstationary
Computer science lcsh:Mechanical engineering and machinery Mechanical Engineering 02 engineering and technology adaptive blind source separation equivariant adaptive source separation via independence 01 natural sciences Blind signal separation variable learning rate Identification (information) Variable (computer science) 020303 mechanical engineering & transports 0203 mechanical engineering Mixing (mathematics) 0103 physical sciences Source separation Separation method lcsh:TJ1-1570 General Materials Science linear time-varying system 010301 acoustics Algorithm Time complexity Independence (probability theory) |
| Zdroj: | Journal of Vibroengineering, Vol 21, Iss 3, Pp 627-638 (2019) |
| ISSN: | 2538-8460 1392-8716 |
| DOI: | 10.21595/jve.2018.20007 |
| Popis: | In the case of multiple nonstationary independent source signals and linear instantaneous time-varying mixing systems, it is difficult to adaptively separate the multiple source signals. Therefore, the adaptive blind source separation (BSS) problem is firstly formally expressed and compared with tradition BSS problem. Then, we propose an adaptive blind identification and separation method based on the variable learning rate equivariant adaptive source separation via independence (EASI) algorithm. Furthermore, we analyze the scope and conditions of variable-learning rate EASI algorithm. The adaptive BSS simulation results also show that the variable learning rate EASI algorithm provides better separation effect than the fixed learning rate EASI and recursive least-squares algorithms. |
| Databáze: | OpenAIRE |
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