## 1 Introduction
Consequently, unsupervised deep learning-based methods have demonstrated their effectiveness in improving the performance of sound anomaly detection by obtaining better latent space features of normal sounds. Among these methods, self-supervised techniques have emerged as one of the most efficient approaches [37; 38]. In recent years, self-supervised learning has gained significant attention for its effectiveness in representation learning. This learning method can be divided into Contrastive and Generative learning. Contrastive learning's objective is to unveil distinctive audio features by comparing positive and negative samples in a high-dimensional space, and Generative self-supervised learning focuses on learning audio representation through the reconstruction of audio signals. Several kinds of research have been proposed to leverage the SSL method of learning for audio representation learning and anomalous sound detection. Examples include AADCL [2], which uses Contrastive learning to learn audio representation; STgram-MFN [39], a spectral-temporal fusion-based self-supervised method; Glow Aff [8] a flow-based self-supervised method; GMADE [3], which uses an ensemble of Group Masked Autoencoders for Density Estimation; CAV-MAE [40], a Contrastive Audio-Visual Masked Autoencoder; and CLP-SCF [37], a Contrastive learning method with self-supervised classification-based fine-tuning. Contrastive learning trains the encoder to be invariant to semantics-preserving data variations, while masked autoencoders (MAEs) [41] focus on learning spatial statistical correlations. Furthermore, MAE methods treat each sample independently in the loss function, whereas Contrastive methods explicitly consider the relationships between all samples in a batch by adjusting embedding distances. Given these differences, we posit that these two approaches are complementary [42], which leads to extracting different discriminative features from a given input. On the other hand, the generalization capability of Contrastive learning is influenced by four crucial factors [13]. The first one is positive sample alignment, which indicates to how well similar audio samples are grouped together in the learned representation space. The second factor is class center divergence, which measures the separation between different classes of audio samples to facilitate better discrimination. The third factor is related to feature collapse prevention, which is essential for learning informative vectors without redundancies. These first three factors mainly pertain to the properties of the learned audio representations. The fourth factor that impacts the generalization capability is the quality of augmented data [13] used during training. Data augmentation techniques play a vital role in creating diverse and representative augmented data, enhancing the model's ability to generalize to unseen samples. To meet the requirement for factor one, we suggest a combined method, aiming to leverage the strengths of both Contrastive learning and masked autoencoders to enhance the model's representational power. To address factor number three, we introduce Variance--Covariance Regularization [43], which is a regularization term added to the Contrastive and MAE loss functions. Additionally, in order to improve factor number four, we incorporate a diffusion-based data augmentation technique. Consequently, our contributions to this research are as follows:  
1. In response to the issue of not having a robust dataset for various real-world noises, we propose a novel diffusion-based data augmentation method. Our approach incorporates textual prompts from ChatGPT as input to AudioLDM. The use of diffusion-based data augmentation allows us to add noise or artifacts with limited knowledge of any target domain solely through text prompts. 2.