# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 4 Proposed Methodology
### Classifier  
In this paper, we use a range of DNN techniques, including ANN, CRNN, and S-CRNN, for the classification of sound events in urban environments. As mentioned in the features extraction stage, we compared and examined both the monaural and binaural features. We used four hidden states in our proposed artificial neural network (ANN). Each training fold recording was split into frames with a length of 0.1 s, which were then further divided into sub-frames of 25 ms; features were extracted from each frame using a hop size of 10 ms. We initially applied a 0.3 s dilation mask to the output values acquired by the ANN. Dilation was used for zeros that were within 0.3 seconds. In addition, we used a CRNN model to train local shift-invariant patterns from the acoustic data. We used a filter size of 3 \(\times\) 3, with batch normalization and max pooling applied after every layer. In order to maintain the temporal resolution of the input, max pooling was only carried out on the frequency axis. In order to learn long-term temporal activity patterns, the layers of CNN are further fed to bidirectional gated recurrent units (Bi-GRU). This is followed by layers of time-distributed fully-connected (dense) layers. In order to produce output with multiple labels, the prediction layer contains a sigmoid activation layer, as shown in Figure 3. The Adam optimizer ([29]) was used, with a learning rate of 0.0001 and a binary cross-entropy loss function [30]. Dropout was used to overcome over-fitting and enhance robustness. The TensorFlow [31] library was used as the backend in Keras, which is an open source software package used to construct artificial neural networks and other design architectures. Figure 3: Block diagram of CRNN model. Here, we summarize Figure 3, which represents the working CRNN model. Monaural and binaural features are retrieved from the input audio signal during feature extraction. The extracted features are sent to the CNN layer, which performs a mathematical activity that involves multiplying two functions (sounds) represented as matrices to create a third function, i.e., the output sound. The output is sent to the Gated Recurrent Unit (bi-GRU), which is designed to exploit connections made through a series of nodes in order to carry out machine learning tasks related to memory and clustering, such as speech recognition. The output of the GRU is subsequently sent via a fully connected (FC) layer, which means that a layer is said to be fully connected if all of its inputs are connected to each activation unit of the layer above it. Next, the Sigmoid layer receives the input, on which it employs a sigmoid function to effectively represent a probability. Its range is 0 to 1, and its domain is only real numbers. The sigmoid layer can be used for network layers other than output layers. Erosion and dilation filters are used in the post-processing layer on the output from the sigmoid layer. Additionally, we employed an S-CRNN which used two concurrent CRNNs with the same set of parameters described above. We refer to this as the S-CRNN model (depicted in Figure 4), as the dataset for the S-CRNN was divided into two subclasses (natural and artificial events), with one subclass fed to one CRNN and the other subclass to the other CRNN. In Figure 4, the urban dataset has been split into two subclasses, subclass A (natural sounds) and subclass B (artificial sounds). We used a distinct feature engineering approach for each of the two subclasses. The same CRNN was applied in parallel to the subclasses in distinct sound applications. The CRNNs employed here are the same as those discussed in Figure 3, except two parallel CRNNs are employed instead of one. For artificial sounds with continuous behavior, such as car sounds, a small size was used for the filter mask; for non-continuous sounds, such as human conversation, a larger length of filter mask was used in the post-processing stage. This is the main advantage of parallel CRNN (that is, S-CRNN).