# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 6 Conclusions  
To improve the foreground speech robustness of DL-based ASC systems, we proposed and evaluated several methods building on insights from previous work on a stochastic classification model for ASC. We systematically investigated the benefits of incorporating noise floor and speech presence probability estimates within a contemporary ResNet-based DNN-framework. Firstly, the experimental study showed that NF-MFBE improves system performance at high SBRs without increasing the complexity of the small-footprint model. In high-complexity architectures, when ASC-relevant information could already be sufficiently extracted from the original MFBE feature, the NF-based feature is then redundant, even degrading the performance. In contrast, by incorporating the speech presence probability, the system performance is significantly improved for both small- and large-footprint models. Owing to a better estimation of speech presence probability at the time-frequency scale, the ASC system can pay more attention to the background-dominant frames to produce more reliable classification results. Next, Ensemble methods bring extra benefit by combining the scores of different systems into one consistent decision, but this improvement in performance comes at the cost of increasing the system complexity. Aiming at reducing the effect of foreground speech, at the SBR of 20 dB, we achieve an absolute improvement in classification accuracy of 9.1% for the small-footprint model and 8.8% for large-footprint model. This investigation can provide a reference for choices in real applications, where the balance between system complexity and performance must be considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Non-Zero Parameters/MACs** & **Small-Footprint Systems** & **Large-Footprint Systems** \\ \hline \(ResNet_{F}\) & 336 KB/1.58 G & 4450 KB/24.72 G \\ \(ResNet_{NF}\) & 336 KB/1.58 G & 4450 KB/24.72 G \\ \(ResNet_{F+NF}\) & 336 KB/1.58 G & 4450 KB/24.72 G \\ NF\_vote\_1 & 672 KB /3.16 G & 8900 KB/49.44 G \\ NF\_vote\_2 & 672 KB /3.16 G & 8900 KB /49.44 G \\ \hline \(ResNet_{F+M}\) & 336 KB/1.58 G & 4450 KB/24.72 G \\ M\_vote & 672 KB/3.16 G & 8900 KB/49.44 G \\ \(ResNet_{F+NF+M}\) & 336 KB/1.58 G & 4450 KB/24.72 G \\ M\_NF\_vote & 1008 KB/4.74 G & 13350 KB/74.16 G \\ Meta\_learner & 1008 KB/4.74 G & 13350 KB/74.16 G \\ \hline \hline \end{tabular}
\end{table}
Table 5: System complexities in terms of non-zero parameters in kilobyte (KB) and Multiply-Accumulate Operations (MACs).  
**Author Contributions:** Conceptualization, N.M.; Data curation, S.S.; Investigation, S.S. and Y.S.; Methodology, S.S. and Y.S.; Software, S.S. and Y.S.; Supervision, N.M.; Validation, S.S.; Writing--original draft, S.S.; Writing--review & editing, Y.S. and N.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.