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eess.AS2023
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder
Jingru Lin, Xianghu Yue, Junyi Ao +1
Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as d…
eess.AS2020★ 16 cited
Semi-supervised learning using teacher-student models for vocal melody extraction
Sangeun Kum, Jing-Hua Lin, Li Su +1
The lack of labeled data is a major obstacle in many music information retrieval tasks such as melody extraction, where labeling is extremely laborious or costly. Semi-supervised l…