# Interaural Coherence Estimation for Speech Processing in Reverberant Environment
## 3 Numerical Experiment and Results
### Optimal Parameter Determination
The results for optimal \(L_{\text{T}}\) and \(\beta\) according to frequency and reverberation time are shown Figure 3. For each of the three reverberation environments, the optimal parameter values for frequency were similar than those of the male samples, but the difference was that \(L_{\text{T}}\) appeared low and \(\beta\) appeared high in the range of 0-4 kHz with 0.69 s of RT\({}_{60}\). Since \(L_{\text{T}}\) represents the length of the quasi-steady interval and \(\beta\) indicates how close the interval is to the steady state, this difference was not considered significant. Likewise, the parameters increased, and \(\beta\) was steady for frequency with increasing RT\({}_{60}\), while \(L_{\text{T}}\) tended to be relatively high at low frequencies. Therefore, it can be seen that there was no significant difference in the parameters according to gender, and we newly determined the optimal \(L_{\text{T}}\) and \(\beta\) according to the reverberation environment and frequency using 4620 speech samples of the training set, as shown in Table 2. It can be seen that the parameters using all speech samples had a consistent tendency and similar values for each gender. These optimal \(L_{\text{T}}\) and \(\beta\) values were applied to the proposed method in this paper, and the performance was compared with that of the conventional IC estimation method. The parameters of the proposed method according to the frequency and reverberation environment were determined to estimate accurate IC in time-frequency bins with the STFT variables mainly used in speech processing. However, the time-frequency bins of the speech may change depending on the STFT variables (window, hop, and FFT length), and it should be checked that the proposed IC estimation method can achieve sufficient estimation performance even when various STFT variables are applied. Figure 3: The optimal \(L_{\text{T}}\) and \(\beta\) of female speech according to frequency intervals in (**a**) 0.37 s, (**b**) 0.69 s, and (**c**) 0.79 s of RT\({}_{60}\) with a 25-ms Hamming window, 10-ms hop length, and 512-point FFT. Figure 2: Optimal \(L_{\text{T}}\) and \(\beta\) of male speech according to frequency intervals in (**a**) 0.37 s, (**b**) 0.69 s, and (**c**) 0.79 s of RT\({}_{60}\) with a 25-ms Hamming window, 10-ms hop length, and 512-point FFT. Thus, the time-frequency bins with a 64-ms Hamming window, a 16-ms hop length, and 1024-point FFT for speech were used in the three different reverberant environments. \(L_{\rm T}\) and \(\beta\) are only related to the quasi-steady interval based on theoretical analysis, and they were not affected by gender in the previous numerical experiment. In this case, we rechecked that there was also no influence of gender; however, the details of rechecking results are skipped in this paper. The optimal parameters for each reverberation environment and frequency using the longer window, hop, and FFT length, as shown in Table 3, were determined based on the averaged MACtotal of the 4620 speech samples of the training set through the same process used for the previous numerical experiment. In the environment with 0.37 s of RT60, \(L_{\rm T}\) was relatively high in the low frequency range. However, \(L_{\rm T}\) showed a steady value of seven according to frequency in other reverberation environments, and \(\beta\) also appeared constant according to frequency in the case of the largest reverberation. In comparison to the previous numerical experiment with short STFT variables, \(L_{\rm T}\) was relatively small, and \(\beta\) was largely changed by \(L_{\rm T}\) because of the long window and hop lengths. Based on these optimal \(L_{\rm T}\) and \(\beta\) values, the estimation performance of the conventional method and the proposed method with the longer STFT length was also compared in this paper.