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

Zero-shot Sound Event Classification Using a Sound Attribute Vector with Global and Local Feature Learning