###### Abstract  
Today's deep learning strategies require ever-increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre-trained for image recognition can classify machine vibrations in the time-frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre-trained for sound recognition, which are inherently suited for time-frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine-tuning the fault diagnosis model. b 2021 202  
infer expected particularities of data in light of the physical phenomena taking place in damaged REBs or, more generally, in defective industrial equipment. Nevertheless, monitoring data are simultaneously influenced by a variety of factors, such as noise and structural resonances. The superimposition of several sources of excitation further affect vibration responses. It may therefore occur that latent paths are hardly detectable in diagnostic features by the inferences of human intelligence, especially in real working conditions. For this reason, artificial intelligence (AI) found fertile ground for applications in vibration-based diagnostics and prognostics. Research on intelligent fault diagnosis (IFD) [5] has consistently populated scientific literature starting from the beginning of this century. At first, machine learning approaches such as the support vector machine (SVM) [17; 18; 19; 20] were mostly investigated. Later, deep learning and neural networks [21] revealed their capabilities in automatically identifying intricated as much as discriminative features in large image datasets [22] upon which machine learning algorithms were in trouble. IFD soon took advantage of these AI tools with the application of autoencoders [23; 24], Convolutional neural networks (CNNs) [25; 26; 27] and Long Short-Term Memory networks (LSTMs) [28] for machinery diagnosis tasks. However, the encouraging results achieved by AI in fault diagnosis may present some limitations: lack of engineering interpretability and lack of a sufficient number of labelled data. The interpretability of AI models is a current research topic in IFD [5; 29] and its insights represent some future trends for research in the upcoming decade. Deep learning achieved unthinkable goals until about a few decades ago in manifold scenarios (e.g., computer vision, medicine, and speech recognition). This is also due to the availability of a sufficient number of labelled data for training deep architectures. However, a significant amount of data for faulty machines is barely available in practical engineering contexts [5]. In such cases, training processes are difficult to manage, since millions of network parameters are extremely prone to overfit small datasets. Thus, the ability to generalize the learned knowledge is intrinsically undermined. Noteworthy research efforts have brought to light benchmark datasets [30; 31; 32; 33], on which several methodologies were tested. Compared with images datasets, it is experimentally challenging to extract millions of data which meaningfully portray probability distributions for machinery diagnosis.