# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 2 Problem Description  
People gather a variety of information through sound, including spatial information about their own location. They constantly perceive the various sounds occurring within a space and simultaneously produce sounds through their activities. As a result, human activities can be recognized solely through sound. In the field of acoustics, recent research [4; 5; 6] has focused on employing machine learning to perceive these sounds in a manner similar to humans, allowing for diverse applications. In particular, recognizing sounds indoors, where most human activities occur, facilitates wide applications. Therefore, in many cases, research on acoustic perception is focused on sounds occurring in domestic situations. For example, since 2016, the DCASE workshop, which focuses on machine learning-based sound recognition, has been conducting tasks (such as SED and ASC) annually with the aim of researching the acoustic perception of sounds occurring indoors and outdoors. In DCASE, datasets for MDA, which target domestic situations, display two distinctive characteristics. First, during the data collection process, neither the labels nor the quantity of data to be collected are predefined. Following data acquisition, labels are then assigned without predetermined constraints, and the dataset's size is not artificially controlled. As a result, some subsets of data are relatively small, as evidenced in the SINS dataset [19]. For example, in the SINS dataset, there are 18,860 10 s audio segments recorded for the \"Absence\" label, while only 972 segments are recorded for \"vacuum cleaning.\" Second, datasets such as SINS [19] and DESED [20] are affected by the frequency characteristics determined by the RTF. The microphone positions and room environments differ between the training environment and the model's deployment environment, resulting in changes in frequency characteristics. Therefore, models need to be robustly trained on the acoustic factors of the microphone's position or the room. To address these two issues, augmentation techniques for data are necessary. For instance, the team of [PERSON] et al. [13], who achieved the first place in the MDA-themed task of DCASE2018 Task5, demonstrated the largest increase in accuracy for the \"other\" label, which had the smallest amount o f data and the lowest baseline accuracy, through data augmentation techniques. Figure 1 displays the number of data per class and the resulting changes in the model's performance, revealing a notable difference between the baseline score and the [PERSON] team's model score for the \"other\" class. The left-side ordinate represents the number of data, while the right-side ordinate represents model accuracy. Additionally, in the evaluation dataset performance of the Inoue model [13], it was revealed that although they achieved an accuracy of 90.4% for data recorded from microphones in the provided locations during training, they only achieved an accuracy of 88.4% for data collected from newly added microphone locations during evaluation, indicating that there was still variance between the microphone locations [6]. In this study, we aimed to conduct data augmentation through RTFs, which is an acoustic factor of indoor environments. Unlike ASC, SED and MDA target indoor sounds, meaning the RTF affects the data. While methods such as directly recording RIRs, using algorithms to generate RIRs [21], or using publicly available RIR datasets [22] can be used for data augmentation, we propose a simpler and cheaper algorithm that employs the statistical properties of RTFs. Specifically, our goal was to harness the acoustic characteristics of sound by leveraging the variability in RTFs corresponding to the microphone's position within the room. Consequently, our proposal uses the existing data and the statistical properties of RTFs for augmentation with RTFs distinct from those in the original dataset. Figure 1: Comparison of the number of data per class, baseline [6] performance per class, and performance of the [PERSON] team [13] model.