# The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition
## 2 Related Works  
### Environmental Sound Recognition  
The intention of the study is to resolve the conditions around Environmental sound recognition (ESR), which is also known as environmental sound classification. The study is not specifically intended to detect the event trigger time precisely, but more important to understand what the acoustic scene is. In past years, numerous methods, such as the Gaussian Mixture Model (GMM) [9], Hidden Markov model (HMM) [10; 11], random forest [12], and support vector machine [13], etc., have been used to solve the ESR problem. However, none of these methods can reach the level of human beings. Since 2012, neural networks have shown the great potential in computer vision [14]. Increasingly, researchers have begun to apply neural networks in the ESR field.  
For a neural network, it is important to choose a suitable feature to be the input value. In 2014, [PERSON] [15] proposed a usable network structure using the log-mel spectrum and delta as the input features, which was once considered state-of-art in the ESR field. The log-mel spectrum has been a popular feature used in the ESR field in recent years. In Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE challenges) [16; 17], most of the researchers still choose to take the log-mel spectrum as one of the network inputs in acoustic scene classification tasks.  
In 2015, [PERSON] et al. [6] used a raw audio wave as the network input to train for speech recognition and had promising results. Raw signals seem to be one of the choices in the ESR field.  
In 2016, [PERSON] et al. [18] proposed SoundNet, which is trained using both images and raw audio. The image part is used to assist in the training, but the scene is still recognized according to the raw audio signals. The result of the network was impressive. Although, the performance might drop considerably, the network structure can still be trainable using the raw audio signal only. In the same year, [PERSON] et al. [19] proposed an 18-layer network that could also work with raw audio signals, and the larger number of filters and a deeper structure provided a much better result using raw audio signals. We could clearly see that network architecture has a huge effect with the raw signal input when comparing these two works [18; 19]. The depth and the filter numbers are obviously worth further discussions. On the other hand, both the two works use the global pooling strategy [20] to integrate the network output information, which has shown an outstanding effect on dimension reduction. Global pooling has other benefits in structure integration, which is explained in our method development.  
In 2017, [PERSON] and [PERSON] [21] proposed EnvNet, which transforms a signal from a raw 1d signal to a 2d spectrogram-like graph through the network. This is an interesting idea, because training with a spectrum might also be adapted to this kind of graph. In the same year, [PERSON] et al. [22] proposed another augmentation method that could be applied in the same kind of network, and the results could even reach the level of human beings.  
These related works reveal that the input features greatly influence the performance of a network. Although many features have been tried on the network, a proper way to combine individual acoustic features are lacking. Moreover, network architectures that use raw signals as the input also require further discussion. Therefore, based on the existing research, this study focuses on improving the above-mentioned aspects.