## 3 Results  
Table 1 presents the results of an experimental study where we used different RNN types (bidirectional LSTM, LSTM and GRU) architectures to simulate the reverberated signal in a single frequency band. The aim of this study was to investigate which RNN architecture can be used for reverberant signal modeling and how the size of the recurrent layers affects the results. As can be seen from Table 1, RNN structures with more parameters, such as LSTM and BiLSTM, show a stable increase in R-squared as we increase the size of the hidden layer (the number of recurrent unit cells in the layer). The GRU-based RNN structure showedunstable results after training, so that in some experimental studies the GRU-based model was not used at all (see Table 2). To compare the flexibility of the selected RNNs in learning individual frequency bands of the reverberated signal, we trained 30 structures (each of the 10 octaves of the human audible frequency spectrum was divided into three bands). We used RNN models with 20 cells in the first hidden layer and 40 cells in the second hidden layer, which is the largest structure studied in the first experiment and which showed the best fitting results. Tables 3 and 4 show the results of an experimental study to test whether a model trained to predict the central band of an octave divided into three parts is good enough to predict adjacent frequency bands. We compared the results for 8 different octaves, ignoring only the first and last octaves--frequencies below 40 Hz and above 10 kHz. A noticeable reduction of fit was observed. We can also see from the results that the use of the central frequency band model to predict neighboring frequency bands also depends on the octave \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RNN** & **Layer Size** & **SSE (10\({}^{3}\))** & **RSS [min max]** & **R-Squared** & **RMSE [min max]** \\ \hline \multirow{4}{*}{LSTM} & 10 + 20 & 15.85 & [0.24 65.29] & \(-0.13\) & [3.36 \(\times\) 10\({}^{-5}\) 1.46] \\  & 20 + 20 & 3.67 & [0.23 37.39] & 0.74 & [2.24 \(\times\) 10\({}^{-5}\) 0.66] \\  & 20 + 40 & 1.36 & [0.19 26.03] & **0.90** & [1.57 \(\times\) 10\({}^{-5}\) 0.56] \\ \hline \multirow{4}{*}{BiLSTM} & 10 + 20 & 6.43 & [0.15 43.44] & 0.54 & [1.18 \(\times\) 10\({}^{-4}\) 0.81] \\  & 20 + 20 & 2.71 & [0.17 22.16] & 0.81 & [7.09 \(\times\) 10\({}^{-6}\) 0.37] \\  & 20 + 40 & 2.16 & [0.17 20.38] & **0.85** & [2.06 \(\times\) 10\({}^{-5}\) 0.38] \\ \hline \multirow{4}{*}{GRU} & 10 + 20 & 5.05 & [0.32 29.97] & 0.64 & [1.30 \(\times\) 10\({}^{-4}\) 0.60] \\  & 20 + 20 & 2.45 & [0.21 34.03] & **0.83** & [6.37 \(\times\) 10\({}^{-5}\) 0.60] \\ \cline{1-1}  & 20 + 40 & 3.31 & [0.18 32.59] & 0.77 & [7.99 \(\times\) 10\({}^{-5}\) 0.66] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different RNN structures by varying the number of cells in the layers of the recurrent neural network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RNN** & **Bin Number** & **SSE (10\({}^{4}\))** & **RSS (Mean)** & **R-Squared** & **RMSE (Mean)** \\ \hline \multirow{8}{*}{LSTM} & 1 & 30.5 & 9.59 & 0.63 & 0.1621 \\  & 2 & 52.6 & 12.54 & 0.37 & 0.2272 \\  & 3 & 75.6 & 14.45 & 0.27 & 0.2703 \\  & 4 & 84.3 & 14.74 & 0.27 & 0.2793 \\  & 5 & 65.4 & 14.10 & 0.32 & 0.2545 \\  & 6 &  
chosen. This is an expected result, as we cannot normally achieve a uniform distribution of sound content across all octaves in any real recording dataset. By dividing each octave into 12 parts, we can analyze the half-tone pattern of the reverberated signal. In this part of the study, we first decided to compare the ability to learn from samples for each frequency band separately. The results are shown in Table 5. We again trained three RNN structures with layer sizes of \(20+40\) RNN cells in two hidden layers, respectively. The LSTM and BiLSTM-based models showed relatively stable results, but the GRU-based RNN was difficult to train to be close to matching all 12 frequency bands.