Support data for our paper:
USING UMAP TO INSPECT AUDIO DATA FOR UNSUPERVISED ANOMALY DETECTION UNDER DOMAIN-SHIFT CONDITIONS
ArXiv preprint can be found here. Code for the experiment software pipeline described in the paper can be found here. The pipeline requires and generates different forms of data. Here we provide the following:
- AudioSet_wav_fragments.zip: This is a custom selection of 39437 wav files (32kHz, mono, 10 seconds) randomly extracted from AudioSet (originally released under CC-BY). In addition to this custom subset, the paper also uses the following ones, which can be downloaded at their respective websites:
- DCASE2021 Task 2 Development Dataset
- DCASE2021 Task 2 Additional Training Dataset
- Fraunhofer's IDMT-ISA-ELECTRIC-ENGINE Dataset
- dcase2021_uads_umaps.zip: To compute the UMAPs, first the log-STFT, log-mel and L3 representations must be extracted, and then the UMAPs must be computed. This can take a substantial amount of time and resources. For convenience, we provide here the 72 UMAPs discussed in the paper.
- dcase2021_uads_umap_plots.zip: Also for convenience, we provide here the 198 high-resolution scatter plots rendered from the UMAPs.
For a comprehensive visual inspection of the computed representations, it is sufficient to download the plots only. Users interested in exploring the plots interactively will need to download all the audio datasets and compute the log-STFT, log-mel and L3 representations as well as the UMAPs themselves (code provided in the GitHub repository). UMAPs for further representations can also be computed and plotted.