# The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition
## 5 Conclusions and Perspectives  
This study proposed a 1D-2D network to perform audio classification, using only the raw signal input, as well as obtain the current best result in ESC50 among the networks using only the raw signal input. In the 1D network, we showed that the frame size had the largest effect, and that a deeper network might not be helpful when only using batch normalization. In addition, our parallel network showed great potential in combining different audio features, and the result was better than that for networks taking only one kind of feature individually. The final accuracy level corresponded to that of a human being.  
Although we found that different frame size and network depth settings could affect the performance of a 1D network, the reasons causing these phenomena require more studies. Much research [37; 38; 39] has proposed methods to show the response area in the input graphics of CNN for the classification result or even for certain filters. The deep learning on sequential data processing with [PERSON]'s theorem is more and more important. Fine-grained visual classification tasks, [PERSON] et al. [40] proposed a novel probability fusion decision framework (named as PFDM-Net) for fine-grained visual classification. The authors in [41] proposed a novel device preprocessing of a speech recognizer, leveraging the online noise tracking and deep learning of nonlinear interactions between speech and noise. While, [PERSON] et al. [42] showed that such a supervisory loss of function is not optimal in human activity recognition, and they improved the feature discriminative power of the CNN models. These techniques could help find out hot spots in a spectrogram and could also help to generate a highly response audio clip for certain layers, which could provide a good direction for analyzing the true effect behind each kind of setting.  
Most of our work focused on the arrangement of a 1D network, and there are still some topics for a 2D network that need to be discussed, such as network depth, filter size, and layer placement. These topics are all good targets for future work.  
Our parallel network combined the raw audio input and log-mel spectrum successfully. More input features could also be tried in the future, such as mel-frequency cepstral coefficients (MFCC) and gammatone spectral coefficients (GTSC), etc. The feature vector ratio is also a good topic for future discussion. With the help of global pooling, we could even combine different kinds of network structures to perform the parallel test, just like the diverse fusion network structures used in computer visual. Our parallel methods have excellent research potential.  
Conceptualization, Y.-K.L. and Y.-Z.H.; Methodology, Y.-K.L., M.-C.S.; Software, L.-K.L.; Validation, Y.-K.L. and M.-C.S.; Formal Analysis, Y.-K.L.; Investigation, Y.-K.L.; Resources, M.-C.S.; Data Curation, Y.-K.L.; Writing Original Draft Preparation, Y.-K.L.; Writing Review & Editing, Y.-K.L., M.-C.S. and Y.-Z.H.; Visualization, Y.-K.L.; Supervision, M.-C.S.; Project Administration, M.-C.S., Y.-Z.H.; Funding Acquisition, Y.-Z.H. and M.-C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Ministry of Science and Technology, R.O.C] grant number [MOST 107-2221-E-019-039-MY2, MOST 109-2221-E-019-057-, MOST 109-2634-F-008-007-, 109-2634-F-019-001-, 109-2221-E-008-059-MY3, 107-2221-E-008-084-MY2 and 109-2622-E-008-018-CC2]. This research was funded by [University System of Taipei Joint Research Program] grant number [USTP-NTUT-NTOU-109-01] and NCU-LSH-109-B010.  
The authors declare no conflict of interest.