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

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

Referências

Eric Clarke. Empirical methods in the study of performance. In Empirical musicology: Aims, methods, prospects, pages 77–102. 2004.

Alf Gabrielsson. Music performance research at the millennium. Psychology of Music, 31(3):221–272, 2003.

C Palmer. Music performance. Annual review of psychology, 48:115–38, 1997.

Campolina, Thiago A. M. and Mota, Davi A. and Loureiro, Mauricio A. Expan: a tool for musical expressiveness analysis. In Proceedings of the 2nd International Conference of Students of Systematic Musicology, pages 24–27, 2009.

Stéfan van der Walt, S Chris Colbert, and Gael Varoquaux. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2):22–30, 2011.

Eric Jones, Travis Oliphant, Pearu Peterson, and Others. SciPy: Open source scientific tools for Python, 2001.

Len Bass, Paul Clements, and Rick Kazman. Software Architecture in Practice. Addison-Wesley Publishing Company, third edit edition, 2013.

P De La Cuadra. Efficient pitch detection techniques for interactive music. In ICMC, pages 403–406, 2001.

Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018, 2018.

G. Peeters, B.L. Giordano, P. Susini, N. Misdariis, and S. McAdams. The timbre toolbox: extracting audio descriptors from musical signals. 130(5), 2011.

Juan Pablo Bello, Laurent Daudet, Samer Abdallah, Chris Duxbury, Mike Davies, and Mark B. Sandler. A tutorial on onset detection in music signals. IEEE Transactions on Speech and Audio Processing, 13(5):1035–1046, 2005.

Tae Hong Park. Introduction to digital signal processing: Computer musically speaking. World Scientific Publishing Co. Pte. Ltd., 2010.

Simon Dixon. Onset Detection Revisited. In 9th International Conference on Digital Audio Effects, pages 133–137, Montreal, Canada, 2006.

Alexander Lerch. An introduction to audio content analysis: Applications in signal processing and music informatics. 2012. 17th Brazilian Symposium on Computer Music - SBCM 2019

Aik Ming Toh, Roberto Togneri, and Sven Nordholm. Spectral entropy as speech features for speech recognition. Computer Engineering, (1):22–25, 2005.

Adrian Sampson. Audioread. https://github.com/beetbox/audioread, 2011.

Magalhães, Tairone N. and Mota, Davi A. and Neto, Aluizio B. O. and Loureiro, Mauricio A. Análise do vibrato e bending na guitarra elétrica. In Anais do XV Simpósio Brasileiro de Computação Musical, pages 36–47, 2015.
Publicado
25/09/2019
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MAGALHAES, Tairone; BARROS, Felippe; LOUREIRO, Maurício. Iracema: a Python library for audio content analysis. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 17. , 2019, São João del-Rei. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 22-27. DOI: https://doi.org/10.5753/sbcm.2019.10418.