An extensible cluster-graph taxonomy for open set sound scene analysis
This provides a structured approach for sound scene analysis researchers to manage and extend label taxonomies efficiently, though it appears incremental as it builds on existing classification frameworks.
The authors tackled the problem of organizing sound scene labels for open set analysis by introducing an extensible cluster-graph taxonomy, which enables complex scene analysis with tangible descriptors while maintaining integrity across subsets and supersets, demonstrated using DCASE challenge classifications.
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.