# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 2 Related Work  
In recent years, due to the success of deep learning in computer vision, speech recognition, and other related areas, deep neural network models such as CNN and Long Short-Term Memory (LSTM) have been applied to solve the ESC problem. These methods can be further divided by the input features they use.  
The log-mel feature of audio signals is regarded as one of the most powerful features for audio recognition [5], due to its consideration of human auditory perception. It is calculated for each frame of the sound and represents the magnitude of each frequency area [30]. A log-mel feature map can be generated by arranging the log-mel features of each frame along the time axis. Since the feature map of log-mel has locality in both the time and frequency, researchers take the convolutional layer as the basic component of the classification model and classify the feature in a similar way to image classification. This method was proposed by [PERSON] [4] and was first applied in environmental sound classification. He extracted log-mel features in each frame and calculated the first temporal derivative of log-mel to obtain the delta log-mel feature. Then, he treated the two-dimensional feature map constituted by static log-mel and delta log-mel as a two-channel input to CNN for classification, similar to how the RGB images are used in image classification [31]. [PERSON] and [PERSON] [19] used a log-mel feature map as a two-channel input for the network classification model, which was composed of three convolutional layers and one fully connected layer. In addition, he adopted a data augmentation technique to increase the variation of training data to efficiently train the network. This increased the accuracy by 6% on the Urbansound8k dataset compared to [PERSON]'s approach [4].  
Several attempts have been made to achieve the learning of features automatically from raw waveforms for ESC [32; 33; 34; 35; 36]. However, the classification accuracy was not as good as the models using log-mel features [5]. End-to-end learning of audio features has also been explored for speech recognition. Motivated by [PERSON] [20], [PERSON] etc. [21] took the first convolutional layer as a finite impulse response filter bank followed by a nonlinearity mapping layer and applied it directly to the one-dimensional raw waveform. The first convolutional layer is called the time convolution layer (tconv), which is used to reduce changes in time domain, while the second convolution layer (fconv) is used to reduce frequency spectrum changes of the feature vectors and maintain locality features in frequency. The three LSTM layers and two Deep Neural Networks (DNN) layers are used for classification. Trained over 2000 h of speech, the recognition rate of their models, using raw waveforms as input, first matches the performance of the model with the log-mel feature on both voice search tasks. Based on this research, [PERSON] [22] proposed to use deep convolutional neural networks to optimize over long sequences of raw audio waveforms. The experimental results indicated that with up to 18 convolutional layers, the classification accuracy of the CNN model can outperform the model with three convolutional layers by 15%. It could match the performance of CNN models with log-mel features. When the number of convolutional layers reached 34, it could process long raw audio waveforms effectively.  
In this paper, our goal is to improve the recognition performance of the log-mel feature-based prediction model by proposing stacked convolutional neural network models using logmel-CNN and raw-CNN, and then build a hybrid ESC recognition model using DS evidence theory to further improve recognition accuracy of environmental sounds.