# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 5 Conclusions  
This paper addressed sound event recognition in scenarios with limited and noisy indoor abnormal sound data. Given the challenges posed by the size and quality of the existing indoor abnormal sound samples, we proposed a data augmentation method that combines raw audio augmentation with spectral feature enhancement. To simulate a realistic indoor environment, we selected eight common indoor abnormal sound events and introduced four types of indoor background sounds. Furthermore, to achieve better interpretability and generalization ability, we introduced the Embedding layer into the ANN model and optimized the model using the AdamW method. Our results indicate that the TS [0.88, 1.12] and BN [10 dB] and Online Aug combination data augmentation method performed the best, achieving a recognition accuracy of 97.4%. This represents an overall accuracy improvement of 10.6% over the baseline model.  
We also analyzed the sensitivity of various augmentation methods, finding that Time Stretching is more sensitive within the [0.88, 1.12] stretching range, Pitch Shifting performs better within the [\(-\)5, 5] pitch shift range, and Background Noise leads to performance improvements under high SNR conditions. Similarly, we can conclude that this combined data augmentation method outperforms traditional audio augmentation methods or single spectral feature augmentation methods. Of course, there are more indoor anomalous sounds in real life than the above eight categories, and the related scenes are more complex and changeable. This approach may not produce the best results for every indoor anomalous sound. Future work will focus on exploring the multi-modal fusion of indoor anomalous sound recognition methods, make full use of data augmentation techniques, and ultimately improve the generalization and robustness of indoor anomalous sound detection.  
Conceptualization, M.W. and J.X.; software, X.S., J.X., Q.M. and X.L.; data curation, J.X.; writing--original draft preparation, J.X. and Q.M.; writing--review and editing, M.W. and X.S.; funding acquisition, M.W. and X.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (62071135), and Projects from Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, Guilin University of Electronic Technology (CRKL220105 and CRKL200111), also Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing (GXKL06180109).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Sound Types** & **Method Used** & **Accuracy before** & **Accuracy after** \\  & & **Augmentation** & **Augmentation** \\ \hline Environmental sound & Pitch Shifting, Time Stretching, Dynamic & 0.741 & 0.791 \\ classification [21] & Range Compression, Background Noise & & \\ \hline Detecting COVID-19 from deep & Color Transformation and Noise & 0.749 & 0.901 \\ breathing sounds [24] & Addition + DeepShufNet & & \\ \hline North Atlantic right & SpecAugment, Mixup & 0.860 & 0.902 \\ whales upcalls [26] & Background Noise and Time Stretching & 0.931 & 0.971 \\ \hline Underwater acoustic & & & \\ target recognition [29] & DCGAN + ResNet18 & 0.925 & 0.964 \\ \hline Environmental sound & & & \\ classification [34] & Mixed Frequency Masking & 0.924 & 0.937 \\ \hline Indoor anomalous sound & Time Stretching, Background Noise, & & \\ event identification & Spectral Masking + EANN & 0.868 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of various data augmentation techniques.