One of the first hurdles in most deep learning applications is obtaining high quality labelled data, which is usually time consuming and expensive. There are strong models in NLP (BERT), computer vision (DINOv3) and speech recognition (wav2vec), that circumvent this problem using self-supervised learning. Our goal will be to apply/adapt these methods this to anomalous sound detection (ASD) with the purpose of automatic machine failure detection.

The data consists of the normal/anomalous operating sounds of several types of real/toy machines. There are two concrete datasets which can be found at:
-https://zenodo.org/records/3384388
-https://zenodo.org/records/6355122
These include some baseline models which we can use to benchmark our proposals against. We will only use the normal operating sounds to train our model as that is what would be available to us in a real-life scenario. Performance will be measured using a subset of normal together with all anomalous operating sounds.