# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 5 Conclusions  
This work presents a data augmentation algorithm to enhance the performance of domestic activity detection using machine learning. The proposed approach was motivated by the fact that sounds generated indoors are affected by RTFs; moreover, the RTFs remain unknown when the algorithm is deployed on a real application. The influences of these RTFs were used to perform data augmentation based on their statistical characteristics. Accordingly, sounds were simulated from the same source at different locations using a known dataset. The proposed method could be applied to the spectral domain through simple calculations with a low computational burden.  
For the experiments, the model proposed in [13] was employed with a simple CNN architecture and similar training parameters. Then, the proposed method was verified to exhibit comparable or slightly superior performance to the existing method through the F1-Score metric. Specifically, compared to the model before data augmentation, the proposed method showed 0.59% improvement on the development dataset and 0.78% improvement on the evaluation dataset when comprising sounds sourced using the unknown microphone. These results support the hypothesis that the proposed technique can compensate for variations in the location of the input data. Additionally, it was observed that further performance gains could be obtained when combining the proposed method with extant techniques, such as mixup, for handling sounds from unknown locations.  
Nonetheless, the proposed method has two key limitations. First, it is primarily applicable to indoor audio recordings, which restricts its generalizability to other environments, such as in ASC tasks that involve more diverse outdoor soundscapes. Second, it is designed to work only with frequency-domain features, thereby limiting its use in models that process raw audio waveforms directly in end-to-end deep learning frameworks. These constraints may reduce the flexibility of our method compared to other approaches that are more adaptable to various input types and recording conditions.  
Despite these limitations, the proposed technique is confirmed to enhance model generalization in scenarios where the RTFs of the development dataset differ from those of the evaluation dataset. It is expected to improve performance with minimal computational burden when applied to tasks involving indoor audio datasets. The experimental results also suggest that further improvements could be achieved by combining the proposed method with other data augmentation techniques. By using this method, the model is anticipated to demonstrate improved generalization performance even in different environments, with different microphone setups, or with microphones other than those used to acquire the development dataset. Furthermore, our proposed method is expected to be applicable across various fields, such as bioacoustics and SED [29, 30], which involve inference models targeting indoor datasets and utilizing frequency-domain features as input.  
Conceptualization, M.K. and S.L.; methodology, M.K. and S.L.; validation, M.K. and S.L.; writing--original draft preparation, M.K.; writing--review and editing, S.L.; visualization, M.K.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Industrial Technology Innovation Program (20023556, Development of personalized noise mitigation technology and service based on noise simulation to solve the noise problem in personal space) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and by Korea Research Institute for defense Technology planning and advancement (KRIT)--Grant funded by the Korea government (DAPA (Defense Acquisition Program Administration)) (No. KRIT-CT-23-026, Integrated Underwater Surveillance Research Center for Adapting Future Technologies, 2024).