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Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Musical Query-by-Semantic-Description Based on Convolutional Neural Network

Authors : Jing Qin, Hongfei Lin, Dongyu Zhang, Shaowu Zhang, Xiaocong Wei

Published in: Information Retrieval

Publisher: Springer International Publishing

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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.

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previous chapter Leveraging External Knowledge to Enhance Query Model for Event Query
next chapter A Feature Extraction and Expansion-Based Approach for Question Target Identification and Classification
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Metadata
Title
Musical Query-by-Semantic-Description Based on Convolutional Neural Network
Authors
Jing Qin
Hongfei Lin
Dongyu Zhang
Shaowu Zhang
Xiaocong Wei
Copyright Year
2017
Book
Information Retrieval

Print ISBN: 978-3-319-68698-1

Electronic ISBN: 978-3-319-68699-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68699-8_19