## 5 Conclusions  
This paper introduced chaotic masking technology to hide multiple speech signals into random signals generated by the Chen chaotic dynamic system, which provided a guarantee for the secure communication of speech signals in a wireless environment. In the process of multipath channel transmission, the observation signals at the receiving end are the convolutional mixed signals of the multipath source signals due to the influence of the multipath effect. To extract the source speech signal efficiently, the CBSS algorithm was explored in depth. The traditional FD-CBSS algorithm is accompanied by permutation and amplitude ambiguity. For this reason, an additional permutation algorithm for separating sub-signals is needed to improve the separation accuracy of the algorithm. This will undoubtedly increase the computational complexity of the algorithm. Therefore, this paper proposed using an efficient Fast IVA algorithm to achieve the separation or extraction of source speech signals. The algorithm relies on the order of correlation between the frequency points and completes the signal separation according to the order of each frequency  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Reference**[43] & **Fast IVA** \\ \hline Running Time & 13.7 s & 7.9 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: The running time required for the algorithm to complete.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Step** & **Time Complexity** \\ \hline STFT & \(T_{1}=O(NBK)\) \\ Whitening Process & \(T_{2}=O\big{(}N_{p}NBK\big{)}\) \\ Iterative Process & \(T_{3}=O(NBK)\) \\ Normalization Process & \(T_{4}=O(NBK)\) \\ ISIFT & \(T_{5}=O(NBK)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation of the time complexity required by the main program.  
point, eliminating the need for additional sorting steps. The simulation results show that the algorithm is not only effective and fast for blind separation of convolutional mixed signals but also suitable for extraction of noise-like signals. In addition, the application of this algorithm can reduce the overhead and eliminate the permutation operation in the traditional frequency-domain algorithm and reduce the algorithm complexity. In the next work, the algorithm applicability will be extended to investigate the underdetermined model for solving the practical problem that the number of microphones is less than source signals in the process of speech sending and receiving. Therefore, the length of the filter directly affects the degree of convolutional mixing of the source signal.  
Conceptualization, E.W.; methodology, S.G. and M.S.; software, S.G.; validation, S.G., M.S. and J.Y.; literature and data curation, Y.Z. and M.S.; writing--original draft preparation, S.G.; writing--review and editing, S.G. and E.W.; project administration, E.W.; funding acquisition, E.W. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 61801173; the Natural Science Foundation of Heilongjiang Province, China, grant number No.LH2019F048 and The Outstanding Youth Fund Project of Heilongjiang University, grant number No.YJSCX2020-167 HJU.  
Data sharing not applicable.  
The authors declare no conflict of interest.