低电平和高电平音频信息处理的时域自适应算法

IF 0.4 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Dario Sanfilippo
{"title":"低电平和高电平音频信息处理的时域自适应算法","authors":"Dario Sanfilippo","doi":"10.1162/comj_a_00592","DOIUrl":null,"url":null,"abstract":"Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal: “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds.”","PeriodicalId":50639,"journal":{"name":"Computer Music Journal","volume":"45 1","pages":"24-38"},"PeriodicalIF":0.4000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing\",\"authors\":\"Dario Sanfilippo\",\"doi\":\"10.1162/comj_a_00592\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal: “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds.”\",\"PeriodicalId\":50639,\"journal\":{\"name\":\"Computer Music Journal\",\"volume\":\"45 1\",\"pages\":\"24-38\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2021-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Music Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/9808259/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Music Journal","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/9808259/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 2

摘要

摘要在本文中,我们提出了一组用于音频流的低层次和高层次分析的时域算法。其中包括低级别的频谱质心、噪声和频谱扩展,以及高级别的动态性、异质性和复杂性。低级算法提供了对特征的连续测量,并且可以在短分析帧下操作。另一方面,高级算法是在感知和复杂性理论的基础上进行的原创设计,用于分析音乐上有意义的信息,无论是短声音还是具有长期非平凡变化的清晰流。这些算法适用于在需要同时从多个流中提取信息的各种现场表演设置中实现实时音频分析。例如,低级别算法可以部署在自适应代理的大型音频网络中,或者部署在小到大的集成中,用于分析仪器的各种特性以进行计算机辅助性能。此外,高级算法可以被实现为基于遵循音乐知情标准的进化算法的音乐系统中的适应度函数的一部分,或者被实现为评估音乐输出的一些特征的质量的分析工具。这些算法的音乐应用可以在本期《计算机音乐杂志》的一篇配套论文中找到:“用于音乐和声音合成的音频反馈网络中的复杂自适应”
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing
Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal: “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds.”
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computer Music Journal
Computer Music Journal 工程技术-计算机:跨学科应用
CiteScore
1.80
自引率
0.00%
发文量
2
审稿时长
>12 weeks
期刊介绍: Computer Music Journal is published quarterly with an annual sound and video anthology containing curated music¹. For four decades, it has been the leading publication about computer music, concentrating fully on digital sound technology and all musical applications of computers. This makes it an essential resource for musicians, composers, scientists, engineers, computer enthusiasts, and anyone exploring the wonders of computer-generated sound. Edited by experts in the field and featuring an international advisory board of eminent computer musicians, issues typically include: In-depth articles on cutting-edge research and developments in technology, methods, and aesthetics of computer music Reports on products of interest, such as new audio and MIDI software and hardware Interviews with leading composers of computer music Announcements of and reports on conferences and courses in the United States and abroad Publication, event, and recording reviews Tutorials, letters, and editorials Numerous graphics, photographs, scores, algorithms, and other illustrations.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信