# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 5 Discussion
### Architecture of the Recognition Model  
One of the major contributions of this paper is the special CNN architecture and its parameter settings, as shown in both MelNet and RawNet in Figure 1. Here, we analyze the influence of the CNN architecture on the final recognition accuracy. We trained several different CNN networks, and used the test results on three datasets to analyze the influence of different network structures on the recognition performance with both MelNet and RawNet.  
First, we tested the CNN architectures with 3, 5, and 7 convolution layers, as shown in Figure 2. The number of filters in the three-layer network (Figure 2a) are set to 24, 48, and 64. The sizes of the receptive fields of the three convolutional layers are set to (6,6), (5,5), and (4,4). The five-layer network (Figure 2b) uses the strategy of repeated stacking so that the first two layers have a filter size of (6,6) and the next two layers have a filter size of (5,5). The numbers of filters are arranged as 24, 24, 48, 48, and 64 for the five convolutional layers. As for the seven-layer network (Figure 2c), their first two layers adopt a three-layer stack. The receptive field is arranged in the same way as the five-layer network and the filter numbers are set to 24, 24, 24, 48, 48, and 64. During the training stage, the MelNet network takes two-dimensional log-mel features of the audio samples as input and RawNet takes the one-dimensional raw audio signals with 20,480 sampling points (about 1 s) directly as input. For the convolutional layer with 24 filters, the stride size of the filters is set as (1,1) to obtain the feature map. For the layers with 48 and 64 filters, the stride is set as (2,2). In all our layers, no max-pooling is used after the convolutional layers. Finally, a fully connected layer is added to the network for recognition.  
We used a box plot to show the accuracy of the different network structures on different datasets under five-fold cross-validations. The results with the ESC-10 dataset (Figure 3a) show that the architecture of different stacked convolutional neural networks with log-mel feature input has some influence on their recognition accuracy. The best recognition accuracy (five-layer network) is 3.2% higher than the worst recognition accuracy (seven-layer network).  
Figure 2: Different stacked CNN network architectures for ESC. (**a–c**) show the CNN architectures with 3, 5, and 7 convolution layers.  
that for the log-mel feature input, the accuracy of the five-layer network is 10.4% higher than that of the three-layer networks and matches the performance of the seven-layer network. Nevertheless, for the raw waveform feature input, the accuracy reaches the maximum (82.8%) when we adopted a five-layer network. On the ESC-50 dataset (Figure 3b), the recognition accuracy of the five-layer convolutional network is also higher than those of the other two-layer configurations. Therefore, we conclude that increasing the number of stacked network layers under the same feature does not necessarily improve recognition accuracy, and the best performance is reached by the five-layer convolutional networks. It is also found that differently from the log-mel feature input case, the stacking of more convolutional layers has a more obvious effect on the recognition accuracy with the raw waveform feature input, which makes sense due to the fact that more layers are needed to extract effective features from raw audio signals.