## 3 Simulations and Measurements
### Subjective Evaluation  
The 50% speech reception thresholds (\(\text{SRT}_{50}\)) were carried out using the AFC toolbox in MATLAB [27]. For each scenario, a different list of 20 sentences is played for a listener. For each played sentence in the list, there are ten alternatives. The listener was asked to select a word from the alternatives after playing an audio file. The speech level of next sentence is adaptively adjusted to measure \(\text{SRT}_{50}\). The step size of each level is dependent on the number of correctly selected words of the previous sentence. Figure 6: The STOI scores for robustness evaluation scenarios in each room for reverbed unprocessed, reverbed OE-enhanced, and the reverbed enhanced signal with the proposed algorithm. The proposed algorithm similar to the main scenarios in Figure 4 shows improvement for the STOI score in comparison to the reverbed unprocessed and OE-enhanced signals. Figure 8 shows the SRT\({}_{50}\) for eight subjects obtained with the OLSA matrix test for all four room-acoustical scenarios and SSN for the unprocessed, OE, and the proposed approach. In the left panel, median values are shown together with the 25% and 75% quantiles and outliers across eight subjects. The right panel shows the mean values and the standard error across the subject's mean values and the standard error at the most right-hand side is calculated across all subjects and rooms. Considering all rooms, it can be seen in Figure 8b that the intelligibility is enhanced up to 3.5 dB for OE and 5 dB for the proposed approach compared to the unprocessed speech. In the rooms R2 and R3, the proposed approach has a slightly larger effect on intelligibility which may be caused by the midrange values of \(T_{60}\). Data obtained in rooms R2 and R3 for SSN show an improvement of about 1.5 dB and 1 dB for the proposed method in comparison to the OE. The proposed approach shows only a small improvement in Rooms R1 and R4, which is due to the low and very high reverberation times that make more improvements difficult. Room 4, which is a church with a reverberation time of \(T_{60}=5\) s, shows no large difference between the proposed and OE approach. For this room, in spite of the higher reverberation time, the SRTs are lower in comparison to that of rooms R2 and R3. This is mainly because of high direct-to-reverberant ratio in the BRIRs of the church and also the larger azimuth angle difference between source and the noise for BRIRs in the church. Figure 7: (**a**) The predictions of mr-GPSM for robustness evaluation scenarios in the presence of SSN. The SNR- mr-GPSM show up to 3 dB enhancement for OE and up to 10 dB improvement in comparison to the unprocessed signal in low SNR = \(-\)15 dB; (**b**) The predictions of mr-GPSM in the presence of pink noise (PN) for three SNR values. The intelligibility scores (SNR-mr-GPSM) show about 2 dB improvement for OE and about 4 dB improvement using the proposed approach. In Figure 9, the same plots are depicted but this time for the PN interferer. Both the OE and proposed approach shows fairly good improvement in comparison to the unprocessed signal. However, altogether a low improvement of about 1 dB is seen for the proposed approach in comparison to the OE. Only for room 2, there is more than 1 dB improvement. The results of SRT\({}_{50}\) measurements for the robustness evaluation scenarios are shown in Figures 10 and 11 for SSN and PN, respectively. These figures show the SRTs optimized on the main scenario and applied to the robustness evaluation positions. For the SSN in Figure 10, a comparison between the three signals shows that for SSN there is an improvement up to 3.5 dB for the OE and 5.5 dB for the proposed method relative to the unprocessed signal. A similar tendency can be seen in Figure 11 for PN. The data in Figure 11 show an overall improvement of 1.5 to 3 dB for OE and 2 to 3.5 dB for the proposed approach depending on the room.