## 6 Conclusions  
This paper proposed an end-to-end Bayesian ensemble one-dimensional CNN for environmental sound classification, archiving higher accuracy with fewer network trainable parameters. It learns the representation from audio waveform directly without additional features extraction and signal processing. We selected the highest performed models based on Bayesian optimization and fused them through the ensemble mechanism. Baseline models are constructed of a convolution layer followed by batch normalization and max-pooling layer with a fully-connected layer and categorical softmax function. UrbanSound8K benchmarking dataset of 8732 audio samples was used to evaluate the proposed model's performance. Our results indicate that the proposed end-to-end Bayesian Ensemble 1D CNN is superior and more efficient for environment sounds classification applications due to the appropriate hyperparameter selection compared to other state-of-the-art approaches. Sensitivity, specificity, accuracy, precision, recall, F-measure, area under the ROC curve, and the area under the precision-recall curve were used as measurement indices to measure the model's performance. Statistical analysis reveals that the enhancements are significant with a classification accuracy of 94.46% on the UrbanSound8K dataset, which is higher than state-of-the-art end-to-end methods by 5.4%. Future research will look at a more precise method of selecting hyperparameters and additional datasets and repositories. Finding an adaptive way to adjust the searching pounds automatically would be recommended. Furthermore, considering hybrid networks have shown much better performance in other domains.  
Conceptualization, M.G.R.; methodology, M.G.R., S.J.A.; software, M.G.R. and S.J.A.; validation, M.G.R., S.J.A.; formal analysis, M.G.R., N.A., and H.A.; investigation, M.G.R., S.J.A., and A.A.; resources, M.G.R., N.A., H.A., and A.A.; data curation, M.G.R. and A.A.; writing--original draft preparation, M.G.R. and A.B.; writing--review and editing, S.J.A., N.A., H.A., A.B.,  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Approach** & **Year** & **Representation** & **Mean Accuracy** & **\# of Parameters** \\ \hline M18 CNN [47] & 2017 & 1D & 72\% & 3.7 M \\ EnvNet-v2 [63] & 2017 & 1D & 78\% & 101 M \\ RawNet [23] & 2018 & 1D & 87\% & 377 K \\
1D CNN Rand [47] & 2019 & 1D & 87\% & 256 K \\
1D CNN Gamma [47] & 2019 & 1D & 89\% & 550 K \\ Proposed Ensemble 1D CNN & 2021 & 1D & 94\% & 1.9 M \\ \hline \end{tabular}
\end{table}
Table 7: Mean accuracy of different approaches on the UrbanSound8k dataset.  
and M.G.R.; visualization, H.A., N.A.; supervision, S.J.A.; project administration, S.J.A.; funding acquisition, S.J.A. All authors have read and agreed to the published version of the manuscript.  
Research reported in this publication was supported by Fundamental Research Grant Project (FRGS) from the Ministry of Education Malaysia (FRGS/1/2018/ICT02/UTP/03/1) under UTP grant number 015 MA0-013.  
We are grateful to the Editor and three anonymous reviewers for their valuable suggestions and comments, which significantly improved the quality of the manuscript.  
The authors declare no conflict of interest.