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- W2765451199 abstract "We present a new music retrieval system based on query by semantic description (QBSD) system, by which a novel song can be used as query and transformed into semantic vector by a convolutional neural network. This method based on Supervised Multi-class labeling (SML), which a song can be annotated by some semantically meaningful tags and retrieved relevant song in semantically annotated database. CAL500 data set is used in experiment, we can learn a deep learning model for each tag in semantic space. To improve the annotation effect, loss function adjustment algorithm and SMOTE algorithm are employed. The experiment results show that this model can get songs with high semantically similarity, and provide a more nature way to music retrieval." @default.
- W2765451199 created "2017-11-10" @default.
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- W2765451199 date "2017-01-01" @default.
- W2765451199 modified "2023-09-27" @default.
- W2765451199 title "Musical Query-by-Semantic-Description Based on Convolutional Neural Network" @default.
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- W2765451199 doi "https://doi.org/10.1007/978-3-319-68699-8_19" @default.
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