Title: An extensible cluster-graph taxonomy for open set sound scene analysis,
Abstract: We present a new extensible and divisible taxonomy for open set sound scene
analysis. This new model allows complex scene analysis with tangible
descriptors and perception labels. Its novel structure is a cluster graph such
that each cluster (or subset) can stand alone for targeted analyses such as
office sound event detection, whilst maintaining integrity over the whole graph
(superset) of labels. The key design benefit is its extensibility as new labels
are needed during new data capture. Furthermore, datasets which use the same
taxonomy are easily augmented, saving future data collection effort. We balance
the details needed for complex scene analysis with avoiding 'the taxonomy of
everything' with our framework to ensure no duplicity in the superset of labels
and demonstrate this with DCASE challenge classifications.