  This paper introduces a zero-shot sound event classification (ZS-SEC) method
to identify sound events that have never occurred in training data. In our
previous work, we proposed a ZS-SEC method using sound attribute vectors
(SAVs), where a deep neural network model infers attribute information that
describes the sound of an event class instead of inferring its class label
directly. Our previous method showed that it could classify unseen events to
some extent; however, the accuracy for unseen events was far inferior to that
for seen events. In this paper, we propose a new ZS-SEC method that can learn
discriminative global features and local features simultaneously to enhance
SAV-based ZS-SEC. In the proposed method, while the global features are learned
in order to discriminate the event classes in the training data, the
spectro-temporal local features are learned in order to regress the attribute
information using attribute prototypes. The experimental results show that our
proposed method can improve the accuracy of SAV-based ZS-SEC and can visualize
the region in the spectrogram related to each attribute.
