# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 5 Results and Discussion
### Performance Evaluation  
Five systems are trained based on these settings for each complexity:  
1. \(ResNet_{F}\): the ResNet baseline system;
2. \(ResNet_{NF}\): the ResNet system with NF-MFBE;
3. \(ResNet_{F+NF}\): the ResNet system with both MFBE and NF-MFBE;
4. \(ResNet_{F+M}\): the ResNet system with both MFBE and speech mask;
5. \(ResNet_{F+NF+M}\): the ResNet system with MFBE, NF-MFBE, and speech mask. Based on different combinations of these systems, the performance of average voting and meta-learner is evaluated. The classification accuracies of the mentioned systems are presented in Table 3 with the small-footprint model and Table 4 with the large-footprint model. Each column represents a test condition with a fixed SBR of the testing dataset ('_Clean_' represents the testing condition where no foreground speech is presented). the MCT dataset, \(ResNet_{F}\) system achieves reasonable accuracy in clean conditions, while the performance deteriorates as SBRs increase. For high SBR (20 dB), there is a significant degradation of approximately 20%. Similar to our observation in the iVector framework, the ResNet model is also not robust against the presence of foreground speech. Next, \(ResNet_{NF}\) that uses NF-MFBE as the input feature is tested. The rest of the system is kept the same as \(ResNet_{F}\). For all tested SBRs, simply replacing MFBE with NF-MFBE as an input feature does not bring any benefits, and, contrary to what we observed in the case of the iVector system, degrades the classification performance. Since the noise floor is obtained by recursively smoothing the signal periodogram, the background signal features can be distorted. Because the estimate is an extremely smoothed signal spectrum with less spectro-temporal variation, this either leads to overfitting or loss of discriminative information for the classification.Next, we consider different combinations of the NF-MFBE and MFBE. In \(ResNet_{F+NF}\) (the third row of Table 3), the NF-MFBE and MFBE are both used as the input features. This yields marginal improvements at high SBRs and shows slight degradation in low SBR conditions (below 0 dB). Further, two voting systems are also presented in \(NF\_{\_}{\textit{vote}}\_1\) and \(NF\_{\_}{\textit{vote}}\_2\): \(NF\_{\_}{\textit{vote}}\_1\) is the average voting of \(ResNet_{F}\) and \(ResNet_{NF}\), and \(NF\_{\_}{\textit{vote}}\_2\) is the average voting of \(ResNet_{F}\) and \(ResNet_{F+NF}\). We expect a balanced performance with voting systems in different scenes. In both of the systems, the improvement is consistent in all SBRs, and \(NF\_{\_}{\textit{vote}}\_2\) achieves the best performance. The results demonstrate that the NF-based feature does contain information relevant to ASC and integrating this information can further improve the ASC system performance in the small footprint model. Another method we consider is to incorporate the speech presence information in order to treat speech-dominant and noise-dominant frames with different importance, giving more focus to background-dominant frames and less focus to speech-dominant frames. In \(ResNet_{F+M}\), the speech presence probability obtained from CRUSE is utilized as an extra input feature of the model. The accuracy of \(ResNet_{F+M}\) in high SBRs (from 5 dB) is higher than the \(ResNet_{F}\) baseline, which means that sufficient ASC information can already be extracted from the background-dominant frames in each segment. However, in clean or low SBR conditions, the performance of \(ResNet_{F+M}\) is worse. This could be because the speech presence probability introduces more distortion in low SBR conditions, due to inaccurate estimates, thereby losing ASC-relevant information. Due to the different performances of \(ResNet_{F}\) and \(ResNet_{F+M}\), \(M\_{\textit{vote}}\) that averages scores of \(ResNet_{F}\) and \(ResNet_{F+M}\) is introduced. This system shows competitive performance in both low and high SBRs.