Paper Info
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 u0027the taxonomy of everythingu0027 with our framework to ensure no duplicity in the superset of labels and demonstrate this with DCASE challenge classifications.
Year
Venue
Field
2018
arXiv: Sound
Data collection,Subset and superset,Cluster graph,Scene analysis,Information retrieval,Computer science,Automatic identification and data capture,Artificial intelligence,Perception,Extensibility,Machine learning,Open set
DocType
Volume
Citations 
Journal
abs/1809.10047
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Helen L. Bear1307.10
Emmanouil Benetos255752.48