Enriching Ontology with Temporal Commonsense for Low-Resource Audio Tagging

Autor:	Zhang, Zhiling, Zhou, Zelin, Tang, Haifeng, Li, Guangwei, Wu, Mengyue, Zhu, Kenny Q.
Rok vydání:	2021
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.1145/3459637.3482097
Popis:	Audio tagging aims at predicting sound events occurred in a recording. Traditional models require enormous laborious annotations, otherwise performance degeneration will be the norm. Therefore, we investigate robust audio tagging models in low-resource scenarios with the enhancement of knowledge graphs. Besides existing ontological knowledge, we further propose a semi-automatic approach that can construct temporal knowledge graphs on diverse domain-specific label sets. Moreover, we leverage a variant of relation-aware graph neural network, D-GCN, to combine the strength of the two knowledge types. Experiments on AudioSet and SONYC urban sound tagging datasets suggest the effectiveness of the introduced temporal knowledge, and the advantage of the combined KGs with D-GCN over single knowledge source. Comment: CIKM 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.01009 Zobrazit plný text záznamu View this record from Arxiv