## 6 Experimental Evaluation
### Experimental Results: Performance of RTF Estimators
\begin{table}
\begin{tabular}{c l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Place.**} & \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**PESQ**} & \multicolumn{8}{c}{**STOI (\%)**} \\ \cline{3-14}  & & \multicolumn{8}{c}{**SNR (dB)**} & \multicolumn{8}{c}{**SNR (dB)**} \\  & & **20** & **15** & **10** & **5** & **0** & \(-\)**5** & **20** & **15** & **10** & **5** & **0** & \(-\)**5** \\ \hline \multirow{4}{*}{CT} & Noisy & 2.26 & 1.80 & 1.46 & 1.23 & 1.11 & 1.07 & 95.36 & 91.01 & 83.82 & 74.05 & 62.48 & 51.46 \\  & eKF-MCRA & 2.28 & 1.84 & 1.49 & 1.26 & 1.13 & 1.08 & 95.58 & 91.91 & 85.37 & 75.57 & 63.65 & 52.20 \\  & eKF-CDR & 2.42 & 2.00 & 1.63 & 1.35 & 1.18 & **1.12** & 93.37 & 89.66 & 83.13 & 73.80 & 62.11 & 50.90 \\  & 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** \\  & eKF-P\&C & 2.59 & 2.07 & 1.66 & 1.37 & 1.19 & 1.11 & 96.90 & 93.59 & 87.60 & 78.56 & 66.85 & 54.84 \\  & _eKF-OracleN_ & _2.76_ & _2.21_ & _1.76_ & _1.44_ & _1.23_ & _1.12_ & _97.76_ & _95.18_ & _90.26_ & _82.35_ & _71.43_ & _59.49_ \\ \hline \multirow{4}{*}{FT} & Noisy & 2.38 & 1.89 & 1.51 & 1.26 & 1.11 & 1.07 & 94.65 & 89.91 & 82.52 & 72.69 & 61.09 & 50.09 \\  & eKF-MCRA & 2.35 & 1.90 & 1.52 & 1.27 & 1.13 & 1.07 & 94.47 & 90.48 & 83.71 & 73.65 & 61.11 & 49.69 \\ \cline{1-1}  & eKF-CDR & 2.57 & 2.08 & 1.66 & 1.36 & 1.16 & 1.08 & 94.80 & 90.79 & 83.77 & 73.75 & 61.24 & 49.41 \\ \cline{1-1}  & eKF-PLDn & 2.43 & 2.03 & 1.65 & **1.37** & **1.19** & **1.10** & 92.62 & 89.34 & 83.41 & 74.64 & 63.46 & **52.20** \\ \cline{1-1}  & 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** \\ \cline{1-1}  & eKF-OracleN & _2.99_ & _2.41_ & _1.88_ & _1.51_ & _1.26_ & _1.13_ & _97.25_ & _94.68_ & _89.88_ & _82.29_ & _71.64_ & _59.85_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Perceptual Evaluation Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) results for different speech absence probability (SAP) estimators when combined with speech presence probability (SPP)-based extended Kalman filter - relative transfer function (eKF-RTF) estimation for Minimum Variance Distortionless Response (MDVR) beamforming. Results are broken down by both signal-to-noise ratio (SNR) and device placement. to narrow the number of possible system combinations. For a fair comparison, we use the same RTF initialization and the same updating scheme based on SPP for the different systems. From Table 4, we can see that our eKF proposal obtains slightly better results in terms of STOI and much lower speech distortion than the other approaches, particularly in FT conditions. As we previously analyzed in a former study [15], the distortionless property of MVDR involves none or very low speech distortion if an accurate estimate of the RTF is available. Therefore, we can conclude that our estimator tracks better the RTF variability than the other approaches. This is more noticeable in FT conditions, where the reverberation level increases and the secondary microphone captures similar power to the reference one, thereby making the RTF more variable and its tracking more challenging. Furthermore, Table 5 shows the SD results for the different approaches grouped by reverberant environment (averaging by noise environment and SNR level). The oracle results for each reverberant environment are also shown, as they give an upper-bound reference of the performance of the different approaches. This can be useful when different reverberant environments are compared, as some of them include more challenging noise environments (e.g., cafe in low reverberation). It can be observed that the increase of the reverberation level makes the RTF estimation more difficult.