{"title":"一个用于音频内容分析的Python库","authors":"T. Magalhaes, F. B. Barros, M. Loureiro","doi":"10.5753/sbcm.2019.10418","DOIUrl":null,"url":null,"abstract":"Iracema is a Python library that aims to provide models for the extraction of meaningful informationfrom recordings of monophonic pieces of music, for purposes of research in music performance. With this objective in mind, we propose an architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them. In this paper we: (1) introduce some key concepts at the core of the proposed architecture; (2) describe the current functionalities of the package; (3) give some examples of the application programming interface; and (4) give some brief examples of audio analysis using the system.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":"41 1","pages":"127-138"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Iracema: a Python library for audio content analysis\",\"authors\":\"T. Magalhaes, F. B. Barros, M. Loureiro\",\"doi\":\"10.5753/sbcm.2019.10418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Iracema is a Python library that aims to provide models for the extraction of meaningful informationfrom recordings of monophonic pieces of music, for purposes of research in music performance. With this objective in mind, we propose an architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them. In this paper we: (1) introduce some key concepts at the core of the proposed architecture; (2) describe the current functionalities of the package; (3) give some examples of the application programming interface; and (4) give some brief examples of audio analysis using the system.\",\"PeriodicalId\":82472,\"journal\":{\"name\":\"Research initiative, treatment action : RITA\",\"volume\":\"41 1\",\"pages\":\"127-138\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research initiative, treatment action : RITA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5753/sbcm.2019.10418\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research initiative, treatment action : RITA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/sbcm.2019.10418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iracema: a Python library for audio content analysis
Iracema is a Python library that aims to provide models for the extraction of meaningful informationfrom recordings of monophonic pieces of music, for purposes of research in music performance. With this objective in mind, we propose an architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them. In this paper we: (1) introduce some key concepts at the core of the proposed architecture; (2) describe the current functionalities of the package; (3) give some examples of the application programming interface; and (4) give some brief examples of audio analysis using the system.