[1]陈壮豪,张茂清,郭为安,等.基于序列模型的音乐词曲匹配度智能评估算法[J].智能系统学报,2020,15(1):67-73.[doi:10.11992/tis.202001006]
 CHEN Zhuanghao,ZHANG Maoqing,GUO Weian,et al.Music lyrics-melody intelligent evaluation algorithm based on sequence model[J].CAAI Transactions on Intelligent Systems,2020,15(1):67-73.[doi:10.11992/tis.202001006]
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基于序列模型的音乐词曲匹配度智能评估算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年1期
页码:
67-73
栏目:
学术论文—机器感知与模式识别
出版日期:
2020-01-01

文章信息/Info

Title:
Music lyrics-melody intelligent evaluation algorithm based on sequence model
作者:
陈壮豪1 张茂清1 郭为安2 康琦1 汪镭1
1. 同济大学 电子与信息工程学院, 上海 201804;
2. 同济大学 中德工程学院, 上海 201804
Author(s):
CHEN Zhuanghao1 ZHANG Maoqing1 GUO Weian2 KANG Qi1 WANG Lei1
1. College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
2. Sino-German College of Applied Sciences, Tongji University, Shanghai 201804, China
关键词:
音乐词曲情感节奏序列模型歌词编码器旋律解码器词曲匹配解码器词曲匹配度
Keywords:
music lyrics-melodyemotionrhythmsequence modellyrics encodermelody encodermatching decoderlyrics-melody matching degreemusic lyrics-melody matching
分类号:
TP393.04
DOI:
10.11992/tis.202001006
摘要:
情感匹配模型是一种常用于评价词曲匹配程度的方法;然而,单纯地依靠情感匹配模型无法对评价词曲匹配度进行准确的评价。为改善此问题,提出了基于序列模型的词曲匹配度智能评估算法,其综合考虑词曲情感和词曲间节奏关系以给出一个更加准确的词曲评估方法。基于公开词曲同步数据集,构建了音乐情感和节奏正反例模型,并基于此模型将音乐切分成片段;进一步,将歌词和旋律片段分别通过歌词编码器和旋律编码器进行编码,并将编码后具有上下语境的歌词特征和旋律特征输入词曲匹配解码器,解析词曲间特征关系,判断词曲片段匹配程度。仿真结果表明:基于序列模型的词曲匹配度智能评估算法,相对于单纯的情感匹配模型,能够更精确地评价词曲匹配程度,验证了本文提出算法的有效性。
Abstract:
Emotional matching model is a method often used to evaluate the degree of lyrics and melody matching. However, it cannot be accurately evaluated based on the emotion matching model. In order to improve it, this paper proposes an intelligent evaluation algorithm of lyrics-melody matching based on a sequence model, which comprehensively considers the emotion and the rhythm relationship between lyrics and melody to give an evaluation method for more accurate evaluation. Firstly, this paper researches and builds music positive and negative samples considering music emotion and phrase based on the public lyrics-melody paired dataset and divide songs to music pieces. Further, the lyrics and melody fragments are encoded by the lyrics-encoder and the melody-encoder, respectively. And take the encoded lyrics feature and melody feature that are contextualized as the input of the lyrics-melody matching decoder to analyze the characteristic relationship between the lyrics and melody, and then determine the matching degree of the lyrics-melody segment. The experimental results show that the music lyrics-melody matching intelligent evaluation algorithm model based on sequence model can more accurately judge the matching degree of lyrics-melody matching than simple music emotion matching, which verifies the effectiveness of the proposed algorithm.

参考文献/References:

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相似文献/References:

[1]方然,苗夺谦,张志飞.一种基于情感的中文微博话题检测方法[J].智能系统学报,2013,8(03):208.
 FANG Ran,MIAO Duoqian,ZHANG Zhifei.An emotion-based method of topic detection from Chinese microblogs[J].CAAI Transactions on Intelligent Systems,2013,8(1):208.

备注/Memo

备注/Memo:
收稿日期:2020-01-06。
基金项目:国家自然科学基金面上项目(51775385,71371142);国家自然科学基金项目 (71771176,61503287)
作者简介:陈壮豪,硕士研究生,主要研究方向为音乐评价的自动化;张茂清,博士研究生,主要研究方向为进化计算及其应用研究。已发表学术论文10余篇。;汪镭,教授,博士生导师,主要研究方向为群体智能、并行实现技术。出版专著4部,发表学术论文90余篇。
通讯作者:汪镭.E-mail:wanglei@tongji.edu.cn
更新日期/Last Update: 1900-01-01