# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 6 Conclusions  
In this paper, we proposed the DS-CNN, a hybrid convolutional neural network model for environmental sound recognition. It is composed of two stacked convolutional neural networks, MelNet and RawNet, trained on log-mel feature input and raw waveform feature input, respectively. Each of these two networks are composed of five convolutional layers with batch normalization and a fully connected layer. Three public datasets, ESC-10, ESC-50, and Urbansound8k, are used to evaluate the recognition performance of the MelNet, RawNet, and DS-CNN models. The recognition accuracy of the MelNet model is 81.1%, 91%, and 90.2%, respectively, on ESC-50, ESC-10, and Urbansound8k, which is 14.6%, 4.5%, and 11.2% higher than existing CNN methods for ESC, including logmel + CNN (ESC-50, [PERSON] and Harada [5]; ESC-10, [PERSON] and [PERSON] [19]; Urbansound8k, Piczak [4]). Our RawNet model achieved 65.2%, 87.7%, and 85.2% accuracy on the above three datasets, which is also 1.2% and 13.52% higher than that of the state-of-the-art end-to-end methods used on the dataset (ESC-50, Tokozume, 64%; Urbansound8k, [PERSON], 71.68%). These results showed that our CNN models are more effective for sound recognition due to learning better high-level features from the log-mel and raw waveform inputs. Finally, we proposed the DS-CNN model based on DS evidence theory. It combines the MelNet and RawNet models to exploit the complementarity of these two CNN models. We found that DS evidence theory could substantially improve the recognition accuracy for sounds characterized with their steady continuity (such as engine noise) or single sound (gunshot) [5], but not on repeated discrete sounds, which will be a focus of future works to further improve the recognition accuracy of our DS-CNN model.  
Conceptualization, S.L., Y.Y. and J.H. ([PERSON]); Data curation, Y.Y., J.H. ([PERSON]) and J.H. ([PERSON]); Funding acquisition, S.L.; Investigation, Y.Y., S.L. and J.H. ([PERSON]); Methodology, Y.Y., J.H. ([PERSON]), G.L., X.Y. and J.H. ([PERSON]); Software, Y.Y.; Supervision, S.L., J.H. ([PERSON]); Writing--original draft, Y.Y. and J.H. ([PERSON]); Writing--review & editing, Y.Y. and J.H. ([PERSON]).  
Figure 6: Confusion matrix for RawNet (**a**); MelNet (**b**); and DS-CNN (**c**) models evaluated on the Urbansound8K dataset. (**a**) shows the each class accuracy of RawNet; (**b**) shows the each class accuracy of MelNet; (**c**) shows the each class accuracy of fusion model.  
**Funding:** This research was funded by National Natural Science Foundation of China under Grant Nos. 91746116 and 51741101, and Science and Technology Project of Guizhou Province under Grant Nos. [2017]2308, Talents [2015]4011 and [2016]5013, Collaborative Innovation [2015]02.  
**Acknowledgments:** We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Nomenclature**  
DO Dog  
RO Rooster  
CO Cow  
FR Frog  
CA Cat  
CB Chirping-birds  
WD Water-drops  
PW Pouring-water  
TF Toilet-flush  
SN Sneezing  
BR Breathing  
CO Coughing  
FO Footsteps  
BT Brushing-teeth  
SN Snoring  
DS Drinking-sipping  
DK Door Knock  
MC Mouse-click  
DW Door-wood-creaks  
CO Can-opening  
GB Glass-breaking  
CH Chainsaw  
SI Siren  
EN Engine  
TR Train  
CB Church-bells  
AI Airplane  
HS Hand-saw