Original release

Changes to Google's 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
ausioset_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.