# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
###### Abstract  
Indoor abnormal sound event identification refers to the automatic detection and recognition of abnormal sounds in an indoor environment using computer auditory technology. However, the process of model training usually requires a large amount of high-quality data, which can be time-consuming and costly to collect. Utilizing limited data has become another preferred approach for such research, but it introduces overfitting issues for machine learning models on small datasets. To overcome this issue, we proposed and validated the framework of combining the offline augmentation of raw audio and online augmentation of spectral features, making the application of small datasets in indoor anomalous sound event identification more feasible. Along with this, an improved two-dimensional audio convolutional neural network (EANN) was also proposed to evaluate and compare the impacts of different data augmentation methods under the framework on the sensitivity of sound event identification. Moreover, we further investigated the performance of four combinations of data augmentation techniques. Our research shows that the proposed combined data augmentation method has an accuracy of 97.4% on the test dataset, which is 10.6% higher than the baseline method. This demonstrates the method's potential in the identification of indoor abnormal sound events. data augmentation; abnormal indoor sound; acoustic event recognition; Mel frequency cepstral coefficient; convolutional neural network +
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performance of deep learning models always relies heavily on data-driven conditions. In real-world applications, the inherent scarcity of abnormal sound data makes it difficult to meet this condition. In addition, issues like small datasets, inaccurate labels, and imbalanced event types are frequently encountered [19]. In summary, obtaining high-quality audio data to satisfy data-driven supervised or semi-supervised learning is particularly challenging, as it requires careful control of multiple factors to ensure data quality and prevent model overfitting. Therefore, data augmentation plays a crucial role in optimizing the dataset and reducing model overfitting [20]. Early data augmentation methods relied on singular transformations of the original audio, such as time stretching, pitch shifting, dynamic range compression, silence trimming, and background noise addition [21; 22; 23]. These methods often depended on the characteristics of the original data and required manual adjustment of transformation scales based on the context, which could increase computational costs during training. Then, augmentation methods inspired by traditional image processing techniques were adopted, where audio data were first converted into image data, and then image data augmentation was applied [24]. In recent years, the focus has shifted towards spectrogram data augmentation, where data augmentation is applied to the spectrogram features of audio. Techniques in this category include time warping, frequency masking, and time masking [25; 26], which have yielded promising results. Building upon this foundation, enhanced spectrogram augmentation methods like Filteraugment [27] and SpecSub [28] have been introduced. These approaches transform spectrogram features to obtain a broader range of training information, further enhancing recognition accuracy. Additionally, the extension of sound samples can be achieved through the use of Generative Adversarial Network (GAN) models [29]. However, GAN networks are challenging to train, and current techniques still require a substantial amount of training sample data. It is particularly noteworthy that combined data augmentation methods have gained widespread application. [PERSON] [24] achieved excellent recall using a composite data augmentation method that combined color transformations and noise addition applied to images.