Iracema: a Python library for audio content analysis

• Tairone Magalhaes Federal University of Minas Gerais
• Felippe Barros Federal University of Minas Gerais
• Maurício Loureiro Federal University of Minas Gerais

DOI: https://doi.org/10.5753/sbcm.2019.10418

Resumo

This paper introduces the alpha version of a Python library called Iracema, which aims to provide models for the extraction of meaningful information from 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) list 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.

Palavras-chave: Music Expressiveness, Music Information Retrieval, Software Systems and Languages for Sound and Music

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