# Capturing Discriminative Information Using a Deep Architecture in Acoustic Scene Classification
## 5 Result Analysis  
Table 2 compares this study's baseline with the two official baselines from the DCASE community. The DCASE2019 baseline is fed with log Mel-spectrograms and uses convolution and fully-connected layers. Furthermore, the DCASE2020 baseline is given L3-Net embeddings [29] extracted from another DNN and uses fully-connected layers for classification. Our baseline uses mel-spectrograms as inputs, and it uses convolution, batch normalization [30], and Leaky ReLU [31] layers with residual connections [32]. We exploit SE module after each residual block (the model architecture, as well as the performance for each device and scene, are presented in [28]). The results show that our baseline outperforms the DCASE2020 baseline over 10% in terms of classification accuracy.  
Table 3 describes the effectiveness of the proposed approaches when using the LCNN, SE, and CBAM. It also compares the effects of using the mix-up and/or specAugment data augmentation methods. First, ResNet and the LCNN achieve accuracies of 65.1% and 67.1%, respectively, without any data augmentation or deep architecture modules. To optimize the LCNN system, we also adjust the number of blocks, finding that the original LCNN with four blocks achieves the best performance. Second, we validate the effectiveness of data augmentation. The results show that mix-up and specAugment are both effective and using a combination of these two methods obtains optimal results. Third, we apply the deep architecture modules of SE and CBAM. Our analysis of the experimental results reveals that CBAM is slightly better than SE.  
Figure 3 represents the confusion matrices for the entire test set and Table 4 depicts the top five frequently misclassified pairs. There are several improvements for other misclassified pairs, but even among the top-5, we found that the total number of total misclassified  
\begin{table}
\begin{tabular}{l c} \hline \hline
**System** & **Acc (\%)** \\ \hline DCASE2019 baseline [2] & 46.5 \\ DCASE2020 baseline [24] & 51.4 \\ \hline
**Ours-baseline** & 65.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline comparison with other systems. Classification accuracies reported using DCASE2020 fold1 configuration.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**System** & **Config** & **Acc (\%)** \\ \hline ResNet & - & 65.1 \\ ResNet(baseline) & mix-up & 65.3 \\ ResNet & SpecAug & 66.7 \\ ResNet & mix-up+SpecAug & 67.3 \\ \hline LCNN & - & 67.1 \\ LCNN & mix-up & 68.4 \\ LCNN & SpecAug & 69.2 \\ LCNN & mix-up+SpecAug & 69.4 \\ \hline LCNN & SE & 68.0 \\ LCNN & CBAM & 68.3 \\ LCNN & SE+CBAM & 68.2 \\ \hline LCNN & mix-up+SpecAug+SE & 69.8 \\ LCNN (proposed) & mix-up+SpecAug+CBAM & 70.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of LCNN, data augmentation, and deep architecture modules.  
pairs is reduced by 17% when compared to the baseline. The number of misclassification errors is reduced except for the pair of \"shopping mall\" and \"street pedestrian\". Interestingly, except for the pair of \"shopping mall\" and \"street pedestrian\", all other classes in each commonly misclassified pair belong to the same categories; those categories include indoor, outdoor, and public transport. Namely, Metro-Tram (public transport), Shopping-Airport (indoor), Shopping-Metro_st (indoor), Public_square-Street_ped (outdoor) have a common category in each pair, whereas shopping mall and street pedestrian are in the indoor and outdoor categories, respectively. This result shows that the proposed architecture can distinguish between the relatively similar classes when using detailed information.  
Table 5 describes the comparison results with the performance of the state-of-the-art systems and model complexity. It should be noted that comparisons are only conducted for a single system. Although we did not achieve the best performance, our system showed comparable results with a few parameters. As we do not exploit complex data preprocessing compared to