  We explore segmentation of known and unknown genre classes using the open
source GTZAN and FMA datasets. For each, we begin with best-case closed set
genre classification, then we apply open set recognition methods. We offer an
algorithm for the music genre classification task using OSR. We demonstrate the
ability to retrieve known genres and as well identification of aural patterns
for novel genres (not appearing in a training set). We conduct four
experiments, each containing a different set of known and unknown classes,
using the GTZAN and the FMA datasets to establish a baseline capacity for novel
genre detection. We employ grid search on both OpenMax and softmax to determine
the optimal total classification accuracy for each experimental setup, and
illustrate interaction between genre labelling and open set recognition
accuracy.
