## 4 Discussion
For the future work these parameters could be selected according to an intelligibility model. For the computational load, there is not much difference between preprocessing for the proposed approach and that of the OE. In both methods, the signal is separated into frequency bands (Gammatone-based in OE and one-third octave band bin separation in the proposed approach) and power spectral of signals are obtained using FFT processing. However, in the proposed approach, there is a significant computational load in the optimization part. The optimization is performed using Sequential Quadratic Programming (SQP) algorithm implemented in MATLAB by running the \"fmincon\" function. Because of using a symbolic function in MATLAB and the complicated optimization algorithm, the running time is high. It is dependent on the T60 and the length of signal. The time needed to calculate the weights of 17 independent bands for a 3 s speech audio file and T60 = 1 s is about 40 min. The running time of OE algorithm for the same file and similar room condition is very low and below 10 s. Therefore, the proposed algorithm with the current optimization approach is not possible to be used in a real-time scenario. Note that this study was focused on the design of algorithm and the reduction of the complexity and computational load will be considered in the future study. We intend to replace the symbolic optimization in MATLAB with a faster algorithm such as a modified version of [28]. The proposed method is in the category of reverbed-base NELE algorithms. According to the literature, algorithms that use priori knowledge of the maskers and RIRs do not perform better than noise-independent algorithms. The ASE and SSDRC approaches that are not using the characteristics of the playback environment outperformed other methods in the NELE challenge [29]. It is surprising that until now enhancement algorithms with the goal of enhancing the noise and reverberation effect on the speech have not performed well. A promising approach could be a combination of three categories of NELE algorithms including rule-based, noise-dependent, and reverberation-dependent to benefit from the advantages of separate methods. For example, in Adaptive Compressive Onset-Enhancement (ACO) method [30], sequential and independent combination of a modified version of the AdaptDRC [6] and the OE [16] is used to enhance the speech in a reverb and noisy room with the knowledge of statistics of additive noise and RIR respectively. **Author Contributions:** Conceptualization, A.F. and S.v.d.P. ; methodology, A.F. and S.v.d.P. ; software, A.F. ; validation, A.F. and S.v.d.P. ; formal analysis, A.F. ; investigation, A.F. ; resources, A.F. and S.v.d.P. ; data curation, A.F. ; writing--original draft preparation, A.F. ; writing--review and editing, S.v.d.P. ; visualization, A.F. and S.v.d.P. ; supervision, S.v.d.P. ; project administration, S.v.d.P. ; funding acquisition, S.v.d.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID: 352015383-SFB 1330 C2. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Carl von Ossietzky University of Oldenburg. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Not applicable. The authors would like to thank [PERSON], [PERSON], and [PERSON] for providing recorded BRIRs and the mr-GPSM speech intelligibility model. The authors also thank the three anonymous reviewers for their helpful comments that helped to improve the paper. The authors declare no conflict of interest.