## 3 Simulations and Measurements
### Objective Evaluation of the Algorithm Using Two Intelligibility Models  
For the objective evaluation of the proposed algorithm, the left ear of the above-mentioned BRIRs were used for the weight computations of the OE and proposed algorithm. The BRIRs were then convolved with OLSA speech material that was either unprocessed, OE-preprocessed, or preprocessed with the proposed method. Two intelligibility models, the STOI [19] and the multi-resolution generalized power-spectrum model (mr-GPSM) [26], were used. In the STOI [19], the clipped temporal envelope of noisy and manipulated speech is compared with that of clean speech using a correlation in 384 ms time intervals as an intermediate intelligibility measure in each one-third octave band. The average of the intermediate intelligibility scores across all time intervals and bands is the STOI intelligibility score which can be a number between zero and one. In the mr-GPSM method [26], using \"speech+noise\" and \"noise\" signals, the Hilbert envelope in each auditory channel is calculated and then low-pass filtered. The signal for each auditory channel is then separated into two independent pathways where the outputs of envelope power SNRs model (EPSM) and power SNRs model (PSM) are calculated. The envelope power SNRs across auditory and modulation channels and power SNRs across auditory channels are first combined and then each of them is multiplied to its empirical correction factor. The final mr-GPSM score is the maximum value of weighted combined envelope power SNRs and weighted combined power SNRs. For both intelligibility models, the intelligibility scores were averaged across 120 sentences. For the STOI, the unprocessed and preprocessed reverbed signals without additive noise are compared with clean speech. The reason for the selection of STOI is the fact that its metric is very similar to our defined cost function using the optimization method. It is expected that by minimizing the cost function in Equation (11) based on the square error, the STOI score based on correlation is also improved. For evaluation with mr-GPSM, the SSN and PN without convolving with BRIRs are added to the reverbed speech materials in three SNR values of \(-15\), \(-10\), and \(0\) dB. This is done to keep the influence of background noise identical across conditions. For each reverbed sentence, different samples of the full noise token are added. To have a good averaging across noise samples, five additional runs for each set of reverbed speech materials are performed. Figure 4 shows the STOI score of the main scenario in each room for reverbed unprocessed, reverbed OE-enhanced, and the reverbed enhanced signal with the proposed algorithm. The OE does not show much difference compared to the unprocessed case, but the improvement for the proposed approach is considerable. The STOI improvement for rooms R1, R2, and R3 is about 0.05. but lower improvement is seen for room R4 with very high reverberation time. Figure 4: The STOI scores for the main scenario in each room for reverbed unprocessed, reverbed OE-enhanced, and the reverbed enhanced signal with the proposed algorithm. The proposed algorithm shows improvement for the STOI score in comparison to the reverbed unprocessed and OE-enhanced signals. Figure 5a shows the predictions of mr-GPSM for SSN. The SNR-mr-GPSM is the intelligibility score of this intelligibility model which is a summation of envelope-SNR and DC-SNR [26]. The speech reception threshold (SRT) is an increasing monotonic function of SNR-mr-GPSM. A comparison between three signals for each panel shows that both OE and the proposed approach enhance the speech intelligibility. However, the improvement for the proposed algorithm is considerable in comparison to the OE method. Specifically for low SNR = \(-\)15 dB, this improvement is about 7 SNR-mr-GPSM for rooms R2 and R3. Generally, the model shows less improvement for rooms R1 and R4.