###### Abstract
Recently, transfer learning (TL) approaches began to address the problem of missing data [5; 34]. The core idea of TL relates to the possibility of applying the knowledge learned in a source task to a target task. TL is inspired by the human brain which can intelligently use knowledge acquired in previous tasks to face new ones quicker or more efficiently [34]. TL strategies applied to machine fault diagnosis typically rely on networks pre-trained using images, to catch low-level features also in pictures drawn from vibration signal processing. This is partially occasioned by the fact that most of deep learning developed around images and CNNs. Consequently, several deep learning applications came across images for formalizing problems of various kinds. One of the largest datasets to train AI models for image recognition is ImageNet ([[https://www.image-net.org/](https://www.image-net.org/)]([https://www.image-net.org/](https://www.image-net.org/)), accessed on 29 October 2021) [35]. Currently, the dataset contains more than 14 million images, and over the past ten years, it has been extensively used to train deep learning architectures in extracting discriminative features of images for classifying them. Applications of knowledge transfer for machine diagnosis using ImageNet are available in the literature. [PERSON] et al. [36] successfully classified machine failures using neural networks pre-trained on ImageNet. [PERSON] et al. [37] showed that a small set of training data is adequate to effectively perform a gear fault diagnosis using deep architectures trained with ImageNet. Instead, [PERSON] et al. [38] and [PERSON] and [PERSON] [39] employed TL to diagnose machine failures, by transferring knowledge between different working conditions. However, no previous studies have investigated knowledge transfer from sound recognition tasks to machine diagnosis. The present work is motivated by the idea that networks pre-trained on audios for sound event detection (SED) [40; 41; 42] may encapsulate the necessary knowledge to classify REB spectrograms. The main goal of SED is to identify instances of sound events in audio recordings [40; 43]. Applications of machine listening are available for instance in the fields of traffic monitoring [44], smart rooms [45], autonomous vehicles [46] and healthcare monitoring [47]. Identifying music, instruments or music genres is among the capabilities of SED networks [48; 49]. The search for bearings characteristic tones in vibration signals is conceptually similar to pitch identification in sound, speech and music recognition. Then, detecting occurrences of bearing failures through vibrations is not so different from identifying sound events in audio signals. It is therefore reasonable to assume that the knowledge required to label spectrograms originated from faulty bearings is partially enclosed in pre-trained SED networks, since some of these have specifically learned to extract dominant patterns in spectrograms. May the knowledge gained in these tasks be transferred to characterize machine vibrations? Is this transfer strategy successful for a small dataset? These are the questions which moved this investigation. Obviously, vibration signals are not audio acquisitions, but they can be pre-processed in similar ways to deal with SED networks. Indeed, SED networks operate with feature spaces pursuant to the human perception of the sound. This study shows that this knowledge transfer efficiently works for a small dataset containing vibration signals. The overall structure of this paper takes the form of six sections. The introduction presented the condition-based monitoring topic and projected AI advantages and issues in the field of machine diagnosis. An overview of CNNs and TL theoretical background is provided in the second section. The third section introduces YAMNet, an efficient CNN for sound detection. This deep architecture is employed in the fourth section for detecting bearing faults in a limited data scenario.