Single channel speech blind separation based on genetic algorithm optimization
| Autor: | Zixi Jia, Ningning Guo, Fei Wang, Haobo Zhao, Yuze Zhang, Wei Wu |
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| Rok vydání: | 2017 |
| Předmět: |
030507 speech-language pathology & audiology
03 medical and health sciences Signal processing Computer science Genetic algorithm Entropy (information theory) White noise 0305 other medical science Blind signal separation Independent component analysis Algorithm Hilbert–Huang transform Time–frequency analysis |
| Zdroj: | 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). |
| Popis: | Blind signal separation (BSS) technology is a new research direction in the field of modern signal processing. In this paper, a single channel speech blind separation method based on time-frequency masking and genetic algorithm optimization is proposed for single-channel speech blind separation. Firstly, the mixed signal is decomposed into an Intrinsic Mode Function (IMF) with different source signal characteristics by using the Ensemble Empirical Mode Decomposition (EEMD) algorithm to compose a new multidimensional signal, and then use the genetic algorithm based on genetic algorithm Optimization of Independent Component Analysis Method to Realize Blind Separation of Signals. The experimental results show that the method can effectively improve the efficiency and stability of the operation and obtain a good separation effect. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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