# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 3 Results
### Experiment 1
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ID** & **BRIR** & **RMS** & **Binaural** & **MFCC** & **Spectral** & \begin{tabular}{c} **RMS +** \\ **Binaural** \\ \end{tabular} & \
\begin{tabular}{c} **MFCC +** \\ **Spectral** \\ \end{tabular} &
\begin{tabular}{c} **All** \\ **Features** \\ \end{tabular} \\ \hline
1 & sbs & 50.8 & 95.8 & 93.3 & 56.7 & 95.8 & 93.3 & 97.5 \\
2 & mair & 54.2 & 94.2 & 84.2 & 47.5 & 94.2 & 81.7 & 95.8 \\
3 & thkcr1 & 50.8 & 98.3 & 85.0 & 45.8 & 98.3 & 83.3 & 98.3 \\
4 & thkcr7 & 37.5 & 95.8 & 90.8 & 60.0 & 95.8 & 91.7 & 96.7 \\
5 & thkbsbs & 42.5 & 84.2 & 75.0 & 48.3 & 84.2 & 75.0 & 84.2 \\
6 & thklbos & 50.8 & 85.8 & 73.3 & 53.3 & 85.8 & 75.0 & 85.8 \\
7 & calypso & 69.2 & 97.5 & 97.5 & 75.8 & 97.5 & 97.5 & 96.7 \\
8 & tvtui & 50.8 & 85.0 & 72.5 & 61.7 & 85.0 & 73.3 & 86.7 \\
9 & tvtui2 & 46.7 & 89.2 & 78.3 & 53.3 & 90.0 & 76.7 & 90.8 \\
10 & labtui & 40.8 & 79.2 & 83.3 & 45.8 & 78.3 & 85.0 & 84.2 \\
11 & rehabtui & 40.8 & 81.7 & 79.2 & 40.8 & 81.7 & 78.3 & 88.3 \\
12 & tuburo250 & 43.3 & 65.0 & 80.8 & 55.8 & 65.0 & 81.7 & 80.8 \\
13 & tuburo310 & 35.8 & 70.8 & 75.0 & 52.5 & 70.8 & 75.0 & 74.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Testing accuracy (%) of the models obtained using subsets of recordings synthesized with individual BRIR sets. Each testing subset consisted of 120 audio excerpts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **RMS** & **Binaural** & **MFCC** & **Spectral** & \begin{tabular}{c} **RMS +** \\ **Binaural** \\ \end{tabular} & \
\begin{tabular}{c} **MFCC +** \\ **Spectral** \\ \end{tabular} &
\begin{tabular}{c} **All** \\ **Features** \\ \end{tabular} \\ \hline Training & 38.2 & 72.9 & 74.9 & 50.0 & 73.1 & 75.1 & 81.2 \\ Testing & 39.9 & 73.1 & 69.0 & 52.2 & 73.6 & 67.8 & 76.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of classification (%) obtained upon training and testing of the model. The training and testing repository consisted of 4368 and 1560 audio excerpts, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **BRIR** & **RMS** & **Binaural** & **MFCC** & **Spectral** & \begin{tabular}{c} **RMS +** \\ **Binaural** \\ \end{tabular} & \
\begin{tabular}{c} **MFCC +** \\ **Spectral** \\ \end{tabular} &
\begin{tabular}{c} **All** \\ **Features** \\ \end{tabular} \\ \hline
1 & sbs & 43.9 & 91.5 & 88.5 & 49.5 & 91.5 & 88.0 & 91.7 \\
2 & mair & 48.5 & 91.8 & 85.9 & 51.6 & 91.8 & 84.9 & 92.2 \\
3 & thkcr1 & 46.3 & 96.8 & 82.1 & 42.1 & 96.8 & 81.6 & 96.2 \\
4 & thkcr7 & 37.9 & 94.8 & 84.2 & 48.2 & 94.9 & 83.8 & 94.7 \\
5 & thkbsbs & 35.9 & 87.9 & 72.5 & 42.5 & 87.9 & 72.4 & 87.6 \\
6 & thklb & 50.2 & 85.4 & 68.8 & 47.7 & 85.3 & 68.1 & 85.7 \\
7 & calypso & 69.3 & 97.5 & 95.2 & 82.7 & 97.5 & 95.6 & 97.8 \\
8 & tvtui & 48.3 & 88.6 & 74.0 & 58.7 & 88.6 & 73.8 & 87.7 \\
9 & tvtui & 45.6 & 85.3 & 80.2 & 47.2 & 85.1 & 79.9 & 86.9 \\
10 & labtui & 40.7 & 79.1 & 71.2 & 44.6 & 79.4 & 70.4 & 78.9 \\
11 & rehabtui & 46.3 & 84.2 & 73.4 & 44.6 & 84.3 & 72.4 & 85.4 \\
12 & tuburo250 & 38.3 & 66.7 & 72.2 & 58.6 & 66.7 & 72.0 & 71.0 \\
13 & tuburo310 & 40.4 & 66.2 & 71.5 & 56.9 & 66.6 & 70.6 & 73.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training accuracy (%) of the models obtained using subsets of recordings synthesized with individual BRIR sets. Each training subset included 336 audio excerpts. For most of the BRIR-specific models, the RMS-based metrics and the spectral features appeared to be of little importance in terms of the classification accuracy. The exception was the model obtained for the BRIR set No. 7 (calypso), for which the accuracy attained for the spectral features and the RMS-based metrics was equal to 75.8% and 69.2%, respectively (see Table 6). Figure 3 illustrates confusion matrices of the selected models. For example, the confusion matrix for the model trained using the audio excerpts synthesized with all the BRIR sets is presented on panel (a) of this figure.