## 6 Experimental Evaluation
### Experimental Results: Performance of Postfiltering Approaches  
Finally, in Table 7 we compare the pWF and OMLSA postfilters with a basic WF postfilter and two other related state-of-the-art dual-channel speech enhancement algorithms intended for smartphones: the PLD-based single-channel WF filter of Reference [17] (PLDwf) and the SPP- and coherence-based single-channel WF filter of Reference [18] (SPPCwf) for CT and FT conditions, respectively. The MVDR-based clean speech PSD estimator (Ms) is used for the different proposed postfilters (WF, pWF and OMLSA). The comparison is done in terms of PESQ and STOI metrics. For CT conditions, both pWF and OMLSA outperform the WF and PLDwf approaches, with OMLSA achieving the best results in noise reduction performance and speech intelligibility. PLDwf achieves more noise reduction (better PESQ) than our WF due to the fact that the former introduces an overestimation of the noise. Such an overestimation also means more speech distortion, so the speech intelligibility is lower compared to our WF approach. The use of SPP information in our postfilters allows for larger noise reduction in frequency bins where speech is absent without additional speech distortion. The availability of accurate SPPs in CT conditions makes OMLSA the best approach in this case, as the LSA estimator performs better than the WF when bins where speech is present are clearly differentiated from those where speech is absent. On the other hand, the SPPs obtained in FT conditions are less accurate for the postfiltering task, thereby degrading the performance of the OMLSA estimator. Nevertheless, this SPP information is still useful for the pWF, which, in general, outperforms the basic WF, especially at higher SNRs. Moreover, both WF and pWF outperform SPPCwf in terms of noise reduction and speech intelligibility. In summary, the availability of accurate RTF and SPP estimates, as those provided by our proposal, clearly helps to improve the performance of postfiltering in FT mode. \begin{table}
\begin{tabular}{l l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Place.**} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**PESQ**} & \multicolumn{6}{c}{**STOI (\%)**} \\ \cline{3-14}  & & \multicolumn{3}{c}{**SNR (dB)**} & \multicolumn{6}{c}{**SNR (dB)**} \\  & & **20** & **15** & **10** & **5** & **0** & \(-\)**5** & **20** & **15** & **10** & **5** & **0** & \(-\)**5** \\ \hline \multirow{4}{*}{CT} & eKF-PLDn & 2.60 & 2.09 & 1.67 & 1.38 & 1.20 & 1.11 & 96.99 & 93.77 & 87.96 & 79.12 & 67.56 & **55.61** \\  & WF-Ps-eKF-PLDn & 2.79 & 2.32 & 1.91 & 1.56 & 1.31 & 1.16 & 97.17 & 94.18 & 88.66 & 80.02 & 67.95 & 54.88 \\  & WF-Ms-eKF-PLDn & **2.81** & **2.34** & **1.95** & **1.62** & **1.36** & **1.20** & **97.18** & **94.20** & **88.72** & **80.11** & **67.97** & 54.51 \\ \hline \multirow{4}{*}{FT} & eKF-P\&C & 2.65 & 2.11 & 1.67 & 1.37 & 1.18 & 1.09 & 95.78 & 91.96 & 85.45 & 76.01 & 64.01 & **52.03** \\  & WF-Ps-eKF-P\&C & 2.59 & 2.22 & 1.85 & 1.54 & 1.31 & 1.17 & 92.59 & 89.56 & 83.75 & 74.60 & 62.70 & 50.25 \\ \cline{1-1}  & WF-Ms-eKF-P\&C & **2.95** & **2.45** & **1.99** & **1.64** & **1.36** & **1.21** & **96.10** & **92.64** & **86.51** & **76.91** & **64.20** & 50.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PESQ and STOI results for different clean speech power spectral density (PSD) estimators when combined with Wiener postfiltering applied to the MVDR beamformer output. Results are broken down by SNR and device placement.