Simulation 1

    

Figure: AM complex tone f₁(t).
Figure: Mixed signals f(t).

  

Figure: SD for $\hat{f}_1(t)$ and the reduced SD of $\hat{f}_1(t)$.

    

Figure: Precision for A_k(t) (SNR=20 dB).
Figure: Extraction property for $\hat{f}_1(t)$ (SNR=20 dB).

    

Figure: Precision for A_k(t) (SNR=0 dB).
Figure: Extraction property for $\hat{f}_1(t)$ (SNR=0 dB).

This simulation assumes that f₁(t) is an AM complex tone as shown in Fig. , where F₀=200 Hz, N_F0=10, and whose amplitude envelope is sinusoidal (10 Hz), and f₂(t) is a bandpassed random noise, where bandwidth of about 6 kHz. Seven types of f(t) are used as simulation stimuli, where the SNRs of f(t) are from -10 to 20 dB in 5-dB steps. Mixed signals in cases of SNR=0 dB and SNR=20 dB are plotted in Fig. .

The simulations were carried out using the seven mixed signals. The average SDs of f₁(t) and f(t), and the mean of the reduced SD of f₁(t) are shown in Fig. . Hence, it is possible to reduce the SD by about 20 dB as noise reduction, using the proposed method. For example, when the SNR of f(t) is 20 dB, the proposed method can segregate A_k(t) with a high precision as shown in Fig. , and can extract the $\hat{f}_1(t)$ shown in Fig. from the f(t) as shown in Fig. . Moreover, when the SNR of f(t) is 0 dB, the proposed method can also segregate A_k(t) as shown in Fig. , and can extract the $\hat{f}_1(t)$ shown in Fig. from the f(t) as shown in Fig. . Hence, the proposed model can extract the amplitude information of signal f₁(t) from a noise-added signal f(t) with a high precision in which signal and noise exist in the same frequency region.