Google's AudioSet: Reformatted

During my work with Google's AudioSet I encountered some problems due to the fact that Weak [1] and Strong [2] versions of the dataset use different csv formatting for the data, and that also labels used in the two datasets are different and also presented in files with different formatting.

This repository aims to unify the formats of the datasets so that it is possible to analyse them in the same pipelines, and also make the dataset files compatible with psds_eval, dcase_util and sed_eval.

The source folder contains also the original dataset files and the script convert.py to regenerate the reformatted files. It can be run from this folder as python src/convert.py, it does not require any external libraries.

NOTE: audioset_weak_train_unbalanced.tsv has been manually split into two files to comply with GitHub's size limit. The Zenodo version has only one big file.

Changes in dataset

All files are converted to tab-separated *.tsv files (i.e. csv files with \t as a separator). All files have a header as the first line.

New fields and filenames

Fields are renamed according to the following table, to be compatible with psds_eval:

Old field	New field
YTID	filename
segment_id	filename
start_seconds	onset
start_time_seconds	onset
end_seconds	offset
end_time_seconds	offset
positive_labels	event_label
label	event_label
present	present

For class label files, id is now the name for the for mid label (e.g. /m/09xor) and label for the human-readable label (e.g. Speech). Index of label indicated for Weak dataset labels (index field in class_labels_indices.csv) is not used.

Files are renamed according to the following table to ensure consisted naming of the form audioset_[weak|strong]_[train|eval]_[balanced|unbalanced|posneg]*.tsv:

Old name	New name
balanced_train_segments.csv	audioset_weak_train_balanced.tsv
unbalanced_train_segments.csv	audioset_weak_train_unbalanced.tsv (split into two files)
eval_segments.csv	audioset_weak_eval.tsv
audioset_train_strong.tsv	audioset_strong_train.tsv
audioset_eval_strong.tsv	audioset_strong_eval.tsv
audioset_eval_strong_framed_posneg.tsv	audioset_strong_eval_posneg.tsv
class_labels_indices.csv	class_labels.tsv (merged with mid_to_display_name.tsv)
mid_to_display_name.tsv	class_labels.tsv (merged with class_labels_indices.csv)

Strong dataset changes

Only changes to the Strong dataset are renaming of fields and reordering of columns, so that both Weak and Strong version have filename and event_label as first two columns.

Weak dataset changes

• Labels are given one per line, instead of comma-separated and quoted list

• To make sure that filename format is the same as in Strong verson, the following format change is made:

The value of the start_seconds field is converted to milliseconds and appended to the filename with an underscore. Since all files in the dataset are assumed to be 10 seconds long, this unifies the format of filename with the Strong version and makes end_seconds also redundant.

Class labels changes

Class labels from both datasets are merged into one file and given in alphabetical order of ids. Since same ids are present in both datasets, but sometimes with different human-readable labels, labels from Strong dataset overwrite those from Weak. It is possible to regenerate class_labels.tsv while giving priority to the Weak version of labels by calling convert_labels(False) from src/convert.py.

License

Both the original dataset and this reworked version are licensed under CC BY 4.0

NOTE Labels come from the AudioSet ontology, which is licensed under CC BY-SA 4.0, but is not replicated in this repository.

Citing this version

This dataset is also available on Zenodo (https://zenodo.org/record/7096702). Please cite that as Bakhtin. (2022). Google's Audioset: Reformatted (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7096702.

References

[1] Gemmeke, J. F., Ellis, D. P., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., ... & Ritter, M. (2017, March). Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 776-780). IEEE.

[2] Hershey, S., Ellis, D. P., Fonseca, E., Jansen, A., Liu, C., Moore, R. C., & Plakal, M. (2021, June). The benefit of temporally-strong labels in audio event classification. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 366-370). IEEE.