# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
###### Abstract  
Monitoring domestic activities helps us to understand user behaviors in indoor environments, which has garnered interest as it aids in understanding human activities in context-aware computing. In the field of acoustics, this goal has been achieved through studies employing machine learning techniques, which are widely used for classification tasks involving sound recognition and other objectives. Machine learning typically achieves better performance with large amounts of high-quality training data. Given the high cost of data collection, development datasets often suffer from imbalanced data or lack high-quality samples, leading to performance degradations in machine learning models. The present study aims to address this data issue through data augmentation techniques. Specifically, since the proposed method targets indoor activities in domestic activity detection, room transfer functions were used for data augmentation. The results show that the proposed method achieves a 0.59% improvement in the F1-Score (micro) from that of the baseline system for the development dataset. Additionally, test data including microphones that were not used during training achieved an F1-Score improvement of 0.78% over that of the baseline system. This demonstrates the enhanced model generalization performance of the proposed method on samples having different room transfer functions to those of the trained dataset. machine learning; acoustic scene classification; monitoring of domestic activity; data augmentation +
Footnote †: journal: Applied sciences
In methods with machine learning approaches, well-organized datasets have a significant impact on model performance and how the statistical properties of the dataset are learned. However, obtaining sufficient reliable data is difficult because it is time-consuming and costly. In situations where obtaining data is challenging due to these difficulties, model performance can deteriorate, or it may even become inappropriate to use a machine learning approach. Additionally, in datasets with unevenly distributed data across classes, performance gaps can occur [12]. To address dataset-related issues, data augmentation has been explored across various fields. In the image domain, there are simple operations such as cropping, rotating, or adding Gaussian noise to the image, and these practices are readily applied in acoustic studies. In acoustics, similar to the image domain, the method of slicing and pasting audio data on the time axis or masking part of the data can increase the performance of the model [13]. Specaugment is a masking technique that has been successfully applied to acoustics [14], in which a random area is masked into the time and frequency axes together with time stretching and warping. However, the effect cannot be guaranteed depending on the task because it is not contrived considering acoustic characteristics [15]. More recently, learning-based approaches (such as Generative Adversarial Networks (GANs)) have been leveraged to generate augmented data by training on existing datasets [16]. However, GAN-based data augmentation has the disadvantage of requiring additional training time and computation power for augmentation. Moreover, if the target dataset differs from the one on which the model was trained, it might not be applicable. In the MDA task of classifying acoustic events within a room, we developed a method that can be broadly applied for detecting scenes within the room by leveraging data augmentation through the room's acoustic properties. Previous studies have demonstrated that the use of room impulse responses (RIRs) is effective in enhancing model performance in indoor acoustic perception tasks [17; 18]. However, this method has limitations in that it requires RIRs measured from real environments or entails additional computational load when simulating new RIRs. The proposed approach builds on this premise and does not require additional training for augmentation, thereby incurring only the minimal computational cost.