Open-Set Tagging Dataset (OST)

Open-set Tagging (OST) is a synthetic dataset of 1s clips used to evaluate source-centric representation learning models in the paper Compositional Audio Representation Learning.

Due to the size of the dataset, we only share the source files, and provide the scripts to generate the dataset are available here.

The dataset generation process is as follows:
1. From single-source FSD50K audio files, we generate a dataset of 10s soundscapes called Open-set Soundscapes (OSS) using Scaper.

2. We then center a 1s window around the center of each sound event in the 10s soundscapes to generate Open-set Tagging (OST), which contains ~500k clips. 

If you are not going to use OSS, you can choose to synthesize it without audio-- this will synthesize only the JAMS annotation files needed for the 1s clips. Using the OSS JAMS files, OST clips can be generated deterministically.

There are five dataset variants (~17GB each), each with a different random assignment of classes to the known and unknown class categories. For further details, refer to our previous paper Multi-label open-set audio classification. In this work, OST dataset variant 1 is referred to as OST for simplicity. 

We also introduce a tiny version of the dataset called OST-Tiny, which contains ~20k clips and only 10 known classes. This is convenient for faster prototyping and to evaluate models in a more challenging open-set classification scenario.