# Acoustic Material Monitoring in Harsh Steelplant Environments
## 4 Classifier
### Cnn  
As previously mentioned, the audio signals are truncated in sub-signals of equal length and the time-frequency representation is computed. Within this work, BPSD features, as proposed in [6; 25], were used as time-frequency representation. BPSD features were computed on a window length of 2048 samples and a hop length of 128 samples. The time-frequency representation serves as an input for a CNN-classifier. Inspired by the model proposed in [5], the deep learning model applied within this paper consists of different types of layers, which are merged into the final predicting model. As shown in Table 2, two convolutional layers with 48 filters serve for feature extraction. Different kernel widths \(k=[3,5,7]\) and number of frequency bins were used to obtain the most appropriate combination. Pooling layers are used for feature reduction and therefore provide a mapping function to lower dimensions [5]. A dropout rate of 0.4 and 0.5 was used to reduce overfitting. Finally, another convolution layer, a fully connected layer with 64 hidden units as in [5] and a softmax layer are applied for obtaining the probability scores of the CNN. Between the convolutional layers, the ReLu-activation function was used. The weights were initialized with the He-Weight Initialization strategy and a training batch size of 32 1-s samples was chosen. The training was done over 100 epochs. For training the cross-categorical loss function and Adam update rule were used. The initial learning rate was set to \(10^{-3}\) with learning rate decays \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The batch size, initial learning rate and initial learning rate decays were chosen by applying a grid search and overall averaged classification accuracy on the validation dataset splits as proposed in [39]. In order to reduce the effect of overfitting and overtraining, early stopping and no-improvement after 10 epochs were applied. In the first step, the previously mentioned CNN-structure was compared using different frequency bins \(n_{bins}=[24,128,512]\), 1 s input window length as in [6; 25] and kernel widths \(k=[3,5,7]\). The comparison was done by means of the overall classification accuracy on five-fold-cross-validation, averaged over the different validation dataset splits. For CNN selection, the training dataset was not processed and the frequency range of 50-3.5 kHz was used because the majority of the surrounding sound like conveyor belt and other environmental sounds are out of these frequency range. In Figure 10 it can be observed that the worst results for all frequency bin \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Type** & **Kernel /Filter/Stride Size** & **Pooling Size** & **Dropout Rate** & **Output Activation Function** \\ \hline L1 & Conv2D & k \(\times\) k/48/1 \(\times\) 1 & & & ReLu \\ L2 & Max Pooling & & 4 \(\times\) 4 & & 0.5 & \\ L3 & Dropout & & & & & ReLu \\ L4 & Conv2D & k \(\times\) k/48/1 \(\times\) 1 & & & 0.4 & \\ L5 & Max Pooling & & & & & ReLu \\ L6 & Dropout & & & & & ReLu \\ L7 & Conv2D & k \(\times\) k/96/1 \(\times\) 1 & & & 0.5 & \\ L8 & Flatten & & & & & \\ L9 & Dropout & & & & & \\ L10 & Dense & 64 & & & & softmax \\ L11 & Dense & 5 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CNN-architecture. configurations were achieved with \(k=3\) and \(k=7\). The best results were achieved with a configuration of \(n_{bins}=128\) and \(k=5\). Hence, this configuration was subsequently used. In a second step, the input window lengths \(w\) for the CNN were varied and the previously mentioned CNN architecture was used. A 10-s sequence was split in equally-sized frames of 5 s, 2 s, 1 s or 500 ms length, resulting in 2, 5, 10 or 20 frames per sequence. From these frames an input representation for the CNN containing 128 frequency bins and 305, 122, 61 or 30 time bins was computed.