# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
###### Abstract
[PERSON] [30] proposed a composite data augmentation method that combined the processing of both original audio and images, where the image processing method employed only salt-and-pepper noise for data augmentation, yielding favorable results. Furthermore, based on the research of [22; 31], individual data augmentation techniques like pitch shifting, background noise, time stretching, and trim silence have been meticulously analyzed for their impact and sensitivity. This approach aims to combine the most effective augmentation techniques. These methods have employed different combined data augmentation strategies, and they have improved the accuracy of acoustic event recognition in relevant scenes. However, for an indoor environment, there are no related data augmentation techniques, and the effective combination of the audio's original features with the audio's spectrogram features within indoor environments is yet to be explored. We have proposed a new data augmentation method and, on this basis, successfully completed the recognition of indoor abnormal sound events. First, indoor abnormal sound data are obtained from the public datasets ESC-50 and UrbanSound8k. Second, by combining techniques for processing the raw audio and spectrogram features, various augmentation techniques such as pitch shifting, background noise, time stretching, and time-frequency masking was applied to enhance the input sound events, resulting in augmented audio data. Finally, a 2D convolutional neural network model was used to recognize abnormal sound events. The embedding technique was introduced to transform sparse high-dimensional feature vectors into dense low-dimensional feature vectors, and the decoupled weight decay Adam algorithm (AdamW) was employed to optimize the model. Our results indicate a significant improvement compared to not using any data augmentation or employing only a single augmentation method. Our contributions can be divided into two parts:1. We studied the impact of different combinations of data augmentation methods on detection sensitivity in indoor abnormal sound environments, which can provide a quantitative reference for subsequent related research. 2. We successfully demonstrated that a data augmentation framework combining offline processing of raw audio with online enhancement of time-frequency masking of spectrograms (spectral features) can effectively improve the recognition ability of deep learning models for indoor abnormal sound in limited datasets. Even in the case of very limited data, it can avoid model overfitting and is of practical value. The structure of this paper is as follows: Section 2 presents the fundamental concepts and principle involved in this research. It provides an overview of the data generation process and offers a detailed explanation of the data augmentation methods and recognition models used in this experiment. Section 3 provides an account of the results obtained from the various experiments. Section 4 is devoted to the analysis and comparison of the experimental results. Finally, Section 5 concludes the paper with some key findings and conclusions.