Playlist-Based Tag Propagation for Improving Music Auto-Tagging

Yi-Hsun Lin, Chia-Hao Chung, Homer H. Chen
{"title":"Playlist-Based Tag Propagation for Improving Music Auto-Tagging","authors":"Yi-Hsun Lin, Chia-Hao Chung, Homer H. Chen","doi":"10.23919/EUSIPCO.2018.8553318","DOIUrl":null,"url":null,"abstract":"The performance of a music auto-tagging system highly relies on the quality of the training dataset. In particular, each training song should have sufficient relevant tags. Tag propagation is a technique that creates additional tags for a song by passing the tags from other similar songs. In this paper, we present a novel tag propagation approach that exploits the song coherence of a playlist to improve the training of an auto-tagging model. The main idea is to share the tags between neighboring songs in a playlist and to optimize the auto-tagging model through a multi-task objective function. We test the proposed playlist-based approach on a convolutional neural network for music auto-tagging and show that it can indeed provide a significant performance improvement.","PeriodicalId":303069,"journal":{"name":"2018 26th European Signal Processing Conference (EUSIPCO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 26th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EUSIPCO.2018.8553318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The performance of a music auto-tagging system highly relies on the quality of the training dataset. In particular, each training song should have sufficient relevant tags. Tag propagation is a technique that creates additional tags for a song by passing the tags from other similar songs. In this paper, we present a novel tag propagation approach that exploits the song coherence of a playlist to improve the training of an auto-tagging model. The main idea is to share the tags between neighboring songs in a playlist and to optimize the auto-tagging model through a multi-task objective function. We test the proposed playlist-based approach on a convolutional neural network for music auto-tagging and show that it can indeed provide a significant performance improvement.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于播放列表的标签传播改进音乐自动标记
音乐自动标记系统的性能高度依赖于训练数据集的质量。特别是,每首训练歌曲都应该有足够的相关标签。标签传播是一种通过传递其他类似歌曲的标签来为歌曲创建额外标签的技术。在本文中,我们提出了一种新的标签传播方法,利用播放列表的歌曲一致性来改进自动标记模型的训练。其主要思想是在播放列表中相邻歌曲之间共享标签,并通过多任务目标函数优化自动标记模型。我们在一个用于音乐自动标记的卷积神经网络上测试了提出的基于播放列表的方法,并表明它确实可以提供显着的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Missing Sample Estimation Based on High-Order Sparse Linear Prediction for Audio Signals Multi-Shot Single Sensor Light Field Camera Using a Color Coded Mask Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms for Sparse Recovery Two-Step Hybrid Multiuser Equalizer for Sub-Connected mmWave Massive MIMO SC-FDMA Systems How Much Will Tiny IoT Nodes Profit from Massive Base Station Arrays?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1