## 4 Simulation Experiment and Result Analysis
### The Influence of Frequency Points in STFT  
When setting the experimental parameters of the STFT, we mention one of the more important parameters, namely the number of frequency points. This is also the number of samples in the short-time window, and this parameter has an important impact on the STFT. The number of frequency points is too small, making the process of STFT too tedious and complicated, increasing the computational complexity of the algorithm. If the number of frequency points is too large, many features of the speech signal will be lost, which will affect the separation or extraction results of the blind extraction algorithm. Therefore, it is especially important to choose a suitable number of frequency points. We will discuss this parameter next, setting the number of frequency points to \(K=[256,512,1024,2048]\). The other experimental conditions are guaranteed to be constant, and the simulation experiments were performed sequentially using the Fast IVA algorithm, with the correlation coefficient as the evaluation criterion. The experimental results are the average data of 20 trials, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **EFICA**[38] & **IF Algorithm**[39] & **Fast IVA** \\ \hline Correlation Coefficient & 0.9532 & 0.9541 & 0.9732 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficient of each separated signal with the source signal.  
Figure 6: The relationship between frequency points and correlation coefficient.  
The effect of the parameter selection of frequency points on the separation result is intuitively reflected in Figure 6. The larger the number of frequency points, the larger the number of samples in the short time window. For a speech signal with a certain length, the fewer frames are obtained by adding windows, which makes more speech features lost and results in a poorer recovered speech signal. With a smaller the number of frequency points, the opposite result is obtained. The experimental results in Figure 6 also confirm this relationship. When the number of frequency points \(K=128\), the average correlation coefficients of the speech estimation signals extracted by this algorithm are all above 0.96, and the separation effect is very good. When the number of frequency points \(K=512\), the correlation coefficients of the estimated signals of speech s1 and s2 can reach about 0.92 and 0.96, respectively. Although the correlation coefficients have decreased, the separation results are still better. When the number of frequency points \(K=1024\) or even larger, the correlation coefficient of the speech estimation signal decreases more obviously, which makes the separation result worse. To ensure that the separation results obtained by this algorithm are better and to avoid too much computation in the STFT process, we compromise by choosing a frequency point \(K=512\). In addition, it can be seen from Figure 6 that the change of frequency points has little effect on the separation results of [PERSON]'s chaotic signal. Therefore, the superiority of the algorithm used in this paper is that it can guarantee the quality of the separated speech signal of good quality while still extracting the chaotic signal of noise class well.