## 1 Introduction  
Maintaining and supervising the performance of industrial machines is an important task in every industry. By effectively analyzing machine behavior, sudden faults can be anticipated and prevented. These analyses can be performed either by experts or by automatic processes. Automatic anomaly sound detection has received significant attention in the research community and numerous methods have been proposed, including one-class classifiers [1], autoencoders [2, 3], Contrastive learning and self-supervised learning (SSL) [4, 5, 6]. These methods are typically trained on datasets that primarily consist of normal sounds [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. Thus, a major challenge in designing an anomaly detector lies in finding a more informative data representation that can effectively differentiate between normal and anomalous sounds, Furthermore, while real-world environments are characterized by domain shifting, diverse noise sources, and various anomalies, there are not any robust data sets for real-world scenarios. In response to the optimal data representation problem, deep learning-based approaches leverage audio augmentation techniques to enhance the model's representation ability [13; 14]. However, most existing audio augmentation methods, such as SpecAugment [15], Time Stretching and Pitch Shifting [16], Noise Injection [17], Reverberation [18], and Time and Frequency Masking [19], have shown limited effectiveness, particularly in modifying high-level semantic attributes in augmented data, such as introducing new environmental sounds or domain-specific characteristic, because these methods often lack diversity and generality along with vital semantic dimensions. In this regard, one popular alternative approach is the use of Generative models, particularly Generative Adversarial Networks (GANs) [20], which have been widely employed for generating synthesized data in computer vision tasks [21; 22]. Synthetic data generated from GANs has demonstrated benefits in representation learning and training classifiers in various applications [20; 23; 24; 25; 26; 27]. Another option in the case of having limited real data is utilizing diffusion models [28]. Although diffusion-based data augmentations have primarily focused on image modality, there is an opportunity to extend their application to audio data. Motivated by this research gap, in this study, we propose a novel diffusion-based data augmentation method for enhancing the generality and diversity of audio datasets in the context of anomaly sound detection. This method leverages textual prompts from ChatGPT [29], which is a Large Language Model (LLM) developed by OpenAI, as input to AudioLDM [30], which is a text-to-audio diffusion model and produces high-quality audio samples. The major challenge in anomaly sound detection is choosing a proper method to improve the performance of this task; earlier works in this field focused on the supervised detection of anomalies [1]. However, a significant problem with supervised anomaly detection is the assumption that all possible anomalous sounds are accessible during the training phase. which may not be feasible in many practical applications. In response to this, unsupervised approaches have been widely explored and in the DCASE 2020 task 2 challenge [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] they have showcased promising results [9; 32]. Researchers used the Hidden Markov Model (HMM) [33], Gaussian Mixture Model (GMM) [34], Non-negative Matrix Factorization (NMF) [35], and deep learning-based approaches, such as autoencoders and Generative models [36], to model and analyze normal sounds. By training these models to compress and reconstruct normal sounds, the underlying properties of normal sounds were learned effectively in the latent space, and when an abnormal sample was introduced into these models, the resulting large reconstruction errors indicated its deviation from the learned normal sound distribution.