## 5 Results and Discussion
### Results
The comparison demonstrates that the ensemble model recognition accuracy is higher, with an average increase of 4.7%. This implies that the ensemble model can learn higher-level and more features from the raw waveform to improve model prediction performance. On average, the Bayesian optimization and ensemble model has enhanced overall performance by 1% and 5%, respectively. Confusion matrices indicate that the CH and ST labels are the most challenging classes for 1D CNN. However, EN and GU classes are well classified by the proposed method. DB, CP, and CH are the classes that show relatively low performance. As can be seen in the confusion matrix before and after enabling, the recognition accuracy varies for each sound form. We only show the confusion matrix of the highest Bayesian model and the proposed ensemble model. Our accuracy reached a maximum of 95.1%, which is higher than that achieved by [PERSON] et al. (2019) [47], and [PERSON] et al. (2018) [23] with accuracy of 89% and 90.2%, respectively. Furthermore, higher than the 73.7% accuracy of [PERSON] (2015) [45], and the 79% of [PERSON] and [PERSON] (2017) [32]. These results indicate that our ensemble 1D CNN has achieved significant sound recognition improvements. It also means that our model structure for ESC with raw waveform input has a higher recognition accuracy. When we applied ensemble theory to merge Bayesian models, the accuracy has increased by an average of 1.26% with each model up to four models and higher accuracy of 5% with the proposed model, proving our model's superiority with the environmental sound events recognition. Our algorithm performance is slightly lower than results reported by [PERSON] et al. (2019) [97] of two-stream CNN with the accuracy of 97%. However, that model was 15.9 M number of trainable parameters six times higher than our proposed method. The results of our experiments on the Urbansoundsk dataset also confirm our findings. The average  
Figure 5: Box-plot showing Bayesian single model accuracy vs. accuracy of ensembles models. accuracy of the 5-cv on the models before the combination was 90.36% and 94.46% after model ensembling. It also shows that ensemble methods can combine various types of predictions to yield high-accuracy results that outperform current state-of-the-art methods. To the best of our knowledge, for the first time, the recognition accuracy has surpassed 94.4% using the 1D CNN as a classifier on UrbanSound8K dataset. It proves our approach superiority in environmental sound recognition applications. Compared to other algorithms, experimental results show that the ensemble learning method enhanced the classification accuracy of the 1D CNN by nearly 5%. Compared with other traditional machine learning algorithms, a more robust lifting algorithm, and a deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. The comparison results for all approaches are summarized in Table 7.  
Our main contribution of this paper is a special 1D CNN architecture and its hyperparameter settings using Bayesian optimization and ensemble learning. We trained several different 1D CNNs with various network configurations to analyze different structures impact on recognition performance. First, we tested the 1D CNN architectures with several convolution layers to analyze their influence on the feature extraction. Ensemble multiple models with different configurations are our second main contribution of this work. Because short audio segments do not contain enough information to train the 1D CNNs properly, this study may be specific to the UrbanSound8K dataset and cannot be gener  
Figure 6: Area under the ROC curve (AUC) of predictor model. Figure 7: Proposed end-to-end Bayesian vs. ensemble model confusion matrix. alized to other audio classification tasks or datasets. However, it is ideal for mobile or handheld devices with limited power due to the low computational complexity of the 1D CNN architecture.