{"title":"Complex Network: A Potential Tool for Uncloaking Tone Quality of Musical Instruments","authors":"S. Sankararaman","doi":"10.1134/S1063771023601231","DOIUrl":null,"url":null,"abstract":"<p>The paper unwraps the potential application of graph features through complex network analysis in assessing the tone quality of a musical note by analysing the two notes, C and G, at different octaves played by the wind instruments, flute and trumpet, as examples. The musical note sound signals are subjected to fast Fourier transform and wavelet analyses to understand harmonic content and its sustainability. The complex network generated from the temporal data helps in understanding the airflow dynamics through the graph features—edge count, graph density and transitivity. The study reveals that the greater the value of network features, the lesser the overtones present in the musical note, suggesting its application in assessing the musical tone quality or timbre<i>.</i></p>","PeriodicalId":455,"journal":{"name":"Acoustical Physics","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acoustical Physics","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1134/S1063771023601231","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
The paper unwraps the potential application of graph features through complex network analysis in assessing the tone quality of a musical note by analysing the two notes, C and G, at different octaves played by the wind instruments, flute and trumpet, as examples. The musical note sound signals are subjected to fast Fourier transform and wavelet analyses to understand harmonic content and its sustainability. The complex network generated from the temporal data helps in understanding the airflow dynamics through the graph features—edge count, graph density and transitivity. The study reveals that the greater the value of network features, the lesser the overtones present in the musical note, suggesting its application in assessing the musical tone quality or timbre.
摘要 本文以长笛和小号这两种管乐器在不同八度演奏的 C 和 G 音为例，通过复杂网络分析揭示了图特征在评估音符音质方面的潜在应用。对音符声音信号进行快速傅里叶变换和小波分析，以了解谐波内容及其可持续性。从时间数据中生成的复杂网络通过图形特征--边数、图形密度和反转性--帮助理解气流动态。研究表明，网络特征值越大，音符中出现的泛音就越少，这表明它可用于评估音质或音色。
期刊介绍：
Acoustical Physics is an international peer reviewed journal published with the participation of the Russian Academy of Sciences. It covers theoretical and experimental aspects of basic and applied acoustics: classical problems of linear acoustics and wave theory; nonlinear acoustics; physical acoustics; ocean acoustics and hydroacoustics; atmospheric and aeroacoustics; acoustics of structurally inhomogeneous solids; geological acoustics; acoustical ecology, noise and vibration; chamber acoustics, musical acoustics; acoustic signals processing, computer simulations; acoustics of living systems, biomedical acoustics; physical principles of engineering acoustics. The journal publishes critical reviews, original articles, short communications, and letters to the editor. It covers theoretical and experimental aspects of basic and applied acoustics. The journal welcomes manuscripts from all countries in the English or Russian language.
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