# Capturing Discriminative Information Using a Deep Architecture in Acoustic Scene Classification
## 3 Proposed Framework
### Regularization and Deep Architecture Modules  
With limited labeled data and recent DNNs with many parameters, overfitting easily occurs in DNN-based ASC systems [3; 12; 18; 19; 23]. To reduce overfitting and enhance the model capacity, our design choices include data augmentation methods and deep architecture modules. For regularization purposes, we adopt two data augmentation methods: mix-up [18] and specAugment [19]. Let \(x_{i}\) and \(x_{j}\) be two audio recordings that belong to class \(y_{i}\) and \(y_{j}\), respectively, where \(y\) is represented by a one-hot vector. A mix-up operation creates an augmented audio recording with a corresponding soft-label using two different recordings. Formally, an augmented audio recording can be denoted as follows:  
\[ \begin{split} x^{\prime}&=\lambda x_{i}+(1- \lambda)x_{j},\\ y^{\prime}&=\lambda y_{i}+(1-\lambda)y_{j},\end{split}  \tag{1}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Kernel/Stride** & **Output** \\ \hline Conv\_1 & \(7\times 3/1\times 1\) & \(l\times 124\times 64\) \\ MFM\_1 & - & \(l\times 124\times 32\) \\ \hline MaxPool\_1 & \(2\times 2/2\times 2\) & (\(l/2\)) \(\times\) 62 \(\times\) 32 \\ \hline Conv\_2a & \(1\times 1/1\times 1\) & (\(l/2\)) \(\times\) 62 \(\times\) 64 \\ MFM\_2a & - & (\(l/2\)) \(\times\) 62 \(\times\) 32 \\ BatchNorm\_2a & - & (\(l/2\)) \(\times\) 62 \(\times\) 32 \\ Conv\_2 & \(3\times 3/1\times 1\) & (\(l/2\)) \(\times\) 62 \(\times\) 96 \\ MFM\_2 & - & (\(l/2\)) \(\times\) 62 \(\times\) 48 \\ CBAM\_2 & - & (\(l/2\)) \(\times\) 62 \(\times\) 48 \\ \hline MaxPool\_2 & \(2\times 2/2\times 2\) & (\(l/4\)) \(\times\) 31 \(\times\) 48 \\ BatchNorm\_2 & - & (\(l/4\)) \(\times\) 31 \(\times\) 48 \\ \hline Conv\_3a & \(1\times 1/1\times 1\) & (\(l/4\)) \(\times\) 31 \(\times\) 96 \\ MFM\_3a & - & (\(l/4\)) \(\times\) 31 \(\times\) 48 \\ BatchNorm\_3a & - & (\(l/4\)) \(\times\) 31 \(\times\) 48 \\ Conv\_3 & \(3\times 3/1\times 1\) & (\(l/4\)) \(\times\) 31 \(\times\) 128 \\ MFM\_3 & - & (\(l/4\)) \(\times\) 31 \(\times\) 64 \\ CBAM\_3 & - & (\(l/4\)) \(\times\) 31 \(\times\) 64 \\ \hline MaxPool\_3 & \(2\times 2/2\times 2\) & (\(l/8\)) \(\times\) 16 \(\times\) 64 \\ \hline Conv\_4a & \(1\times 1/1\times 1\) & (\(l/8\)) \(\times\) 16 \(\times\) 128 \\ MFM\_4a & - & (\(l/8\)) \(\times\) 16 \(\times\) 64 \\ BatchNorm\_3a & - & (\(l/8\)) \(\times\) 16 \(\times\) 64 \\ Conv\_4 & \(3\times 3/1\times 1\) & (\(l/8\)) \(\times\) 16 \(\times\) 64 \\ MFM\_4 & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ CBAM\_4 & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ \hline BatchNorm\_4 & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ \hline Conv\_5a & \(1\times 1/1\times 1\) & (\(l/8\)) \(\times\) 16 \(\times\) 64 \\ MFM\_5a & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ BatchNorm\_5a & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ Conv\_5 & \(3\times 3/1\times 1\) & (\(l/8\)) \(\times\) 16 \(\times\) 64 \\ MFM\_5 & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ CBAM\_5 & - & (\(l/8\)) \(\times\) 16 \(\times\) 32 \\ \hline MaxPool\_5 & \(2\times 2/2\times 2\) & (\(l/16\)) \(\times\) 8 \(\times\) 32 \\ \hline FC\_1 & - & 160 \\ MFM\_FC1 & - & 80 \\ \hline FC\_2 & - & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The LCNN architecture. The numbers in the output shape column refer to the frame (time), frequency, and number of channels. MFM, MaxPool and FC indicate the max feature map, max pooling layer, and fully-connected layer, respectively. where \(\lambda\) is a random value between 0 and 1, drawn from a beta distribution, \(Beta(\alpha,\alpha)\), and \(\alpha\in(0,\inf)\). Despite its simple implementation, the mix-up operation is widely adopted for the ASC task in the literature. In addition, we adopt specAugment [19], which was first proposed for robust speech recognition and masks a certain region of a two-dimensional input feature (i.e., spectrogram, Mel-filterbank energy). Among the three methodologies proposed in [19], we adopt frequency masking and time masking.