• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

数字音乐的特征提取与智能文本生成。

Feature Extraction and Intelligent Text Generation of Digital Music.

机构信息

Department of Music, Henan Finance University, Zhengzhou 450046, Henan, China.

出版信息

Comput Intell Neurosci. 2022 Jul 7;2022:7952259. doi: 10.1155/2022/7952259. eCollection 2022.

DOI:10.1155/2022/7952259
PMID:35845909
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9282989/
Abstract

Because the current network music operation mechanism is constantly improving and the matching of music platforms and users is poor, in this paper, the characteristics of digital music are analyzed, and the music features, rhythm, tune, intensity, and timbre with the MIDI format are extracted. Then, a music feature information extraction algorithm based on neural networks is proposed, and according to the extracted information of the music style, the B2T model is adopted for intelligent text generation. Finally, test results are given by the style matching rate and ROUGE value, which show that the model is accurate and effective for classification of music and description of related text, and the extraction of music feature information has a certain influence on its intelligent text generation.

摘要

由于当前网络音乐运营机制不断完善，音乐平台与用户的匹配度较差，文中分析了数字音乐的特点，提取了具有 MIDI 格式的音乐特征、节奏、旋律、力度和音色。然后，提出了一种基于神经网络的音乐特征信息提取算法，根据音乐风格的提取信息，采用 B2T 模型进行智能文本生成。最后，通过风格匹配率和 ROUGE 值给出了测试结果，表明该模型对于音乐分类和相关文本描述是准确有效的，音乐特征信息的提取对其智能文本生成有一定影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/4e7a9ff5666f/CIN2022-7952259.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/dbf433c64a8d/CIN2022-7952259.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/0a9a471bc9d9/CIN2022-7952259.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/be7c78e205e2/CIN2022-7952259.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/b356fc0d668a/CIN2022-7952259.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/2203d4ef7b3f/CIN2022-7952259.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/c5cd78a52fc4/CIN2022-7952259.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/4e7a9ff5666f/CIN2022-7952259.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/dbf433c64a8d/CIN2022-7952259.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/0a9a471bc9d9/CIN2022-7952259.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/be7c78e205e2/CIN2022-7952259.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/b356fc0d668a/CIN2022-7952259.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/2203d4ef7b3f/CIN2022-7952259.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/c5cd78a52fc4/CIN2022-7952259.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3edf/9282989/4e7a9ff5666f/CIN2022-7952259.007.jpg

相似文献

1
Feature Extraction and Intelligent Text Generation of Digital Music.数字音乐的特征提取与智能文本生成。
Comput Intell Neurosci. 2022 Jul 7;2022:7952259. doi: 10.1155/2022/7952259. eCollection 2022.
2
Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization.变分模糊神经网络算法在音乐智能营销策略优化中的应用。
Comput Intell Neurosci. 2022 Jan 6;2022:9051058. doi: 10.1155/2022/9051058. eCollection 2022.
3
Investigation on the Extraction Methods of Timbre Features in Vocal Singing Based on Machine Learning.基于机器学习的歌唱音色特征提取方法研究。
Comput Intell Neurosci. 2022 Sep 17;2022:5074829. doi: 10.1155/2022/5074829. eCollection 2022.
4
Construction and Simulation of Music Style Prediction Model under Improved Sparse Neural Network.改进稀疏神经网络下的音乐风格预测模型的构建与仿真。
Comput Intell Neurosci. 2022 Apr 8;2022:6268224. doi: 10.1155/2022/6268224. eCollection 2022.
5
Research on Music Style Classification Based on Deep Learning.基于深度学习的音乐风格分类研究。
Comput Math Methods Med. 2022 Jan 18;2022:3699885. doi: 10.1155/2022/3699885. eCollection 2022.
6
Construction of Music Intelligent Creation Model Based on Convolutional Neural Network.基于卷积神经网络的音乐智能创作模型构建。
Comput Intell Neurosci. 2022 Jul 5;2022:2854066. doi: 10.1155/2022/2854066. eCollection 2022.
7
A Multimodal Convolutional Neural Network Model for the Analysis of Music Genre on Children's Emotions Influence Intelligence.用于分析音乐类型对儿童情绪智力影响的多模态卷积神经网络模型。
Comput Intell Neurosci. 2022 Aug 29;2022:5611456. doi: 10.1155/2022/5611456. eCollection 2022.
8
Music Score Recognition Method Based on Deep Learning.基于深度学习的乐谱识别方法
Comput Intell Neurosci. 2022 Jul 7;2022:3022767. doi: 10.1155/2022/3022767. eCollection 2022.
9
Music Composition and Emotion Recognition Using Big Data Technology and Neural Network Algorithm.基于大数据技术和神经网络算法的音乐创作与情感识别
Comput Intell Neurosci. 2021 Dec 16;2021:5398922. doi: 10.1155/2021/5398922. eCollection 2021.
10
Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm.基于混合密度网络算法的音乐剧智能编舞应用研究。
Comput Intell Neurosci. 2021 Nov 29;2021:4337398. doi: 10.1155/2021/4337398. eCollection 2021.

引用本文的文献

1
Retracted: Feature Extraction and Intelligent Text Generation of Digital Music.撤回：数字音乐的特征提取与智能文本生成
Comput Intell Neurosci. 2023 Jul 12;2023:9809451. doi: 10.1155/2023/9809451. eCollection 2023.

本文引用的文献

1
Emotion rendering in music: range and characteristic values of seven musical variables.音乐中的情感表达：七种音乐变量的范围和特征值。
Cortex. 2011 Oct;47(9):1068-81. doi: 10.1016/j.cortex.2011.05.009. Epub 2011 May 17.
2
Domain adaptation via transfer component analysis.通过迁移成分分析实现领域自适应。
IEEE Trans Neural Netw. 2011 Feb;22(2):199-210. doi: 10.1109/TNN.2010.2091281. Epub 2010 Nov 18.