Computer Music Research Group - IME/USP Report for SBCM 2019

  • Fábio Gorodscy University of São Paulo
  • Guilherme Feulo University of São Paulo
  • Nicolas Figueiredo University of São Paulo
  • Paulo Vitor Itaboraí University of São Paulo
  • Roberto Bodo University of São Paulo
  • Rodrigo Borges University of São Paulo
  • Shayenne Moura University of São Paulo

Resumo


The following report presents some of the ongoing projects that are taking place in the group’s laboratory. One of the noteable characteristics of this group is the extensive research spectrum, the plurality of research areas that are being studied by it’s members, such as Music Information Retrieval, Signal Processing and New Interfaces for Musical Expression.

Palavras-chave: Digital Sound Processing, Music Information Retrieval, Sensors and Multimodal Signal Processing

Referências

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Justin Salamon, Joan Serrà, and Emilia Gómez. Tonal representations for music retrieval: from version identification to query-by-humming. Int. J. of Multimedia Info. Retrieval, special issue on Hybrid Music Info. Retrieval, 2(1):45–58, Mar. 2013.

Ernesto López, Martı́n Rocamora, and Gonzalo Sosa. Búsqueda de música por tarareo, 2004.

Bartłomiej Stasiak. Follow that tune – adaptive approach to dtw-based query-by-humming system. ARCHIVES OF ACOUSTICS, 39(4):467–476, 2014.

Alexios Kotsifakos, Panagiotis Papapetrou, Jaakko Hollmen, Dimitrios Gunopulos, Vassilis Athitsos, and George Kollios. Hum-a-song: A subsequence matching with gapsrange-tolerances query-by-humming system. 5(10), 2012.

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Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor. Recommender Systems Handbook. SpringerVerlag, Berlin, Heidelberg, 1st edition, 2010.

Rodrigo Borges and Marcelo Queiroz. Automatic music recommendation based on acoustic content and implicit listening feedback. Revista Música Hodie, 18(1):31 – 43, jun. 2018.

A. L. Berenzweig, D. P. W. Ellis, and S. Lawrence. Using voice segments to improve artist classification of music. In 22nd Int. Conf.: Virtual, Synthetic, and Entertainment Audio. Audio Engineering Society, 2002.

Yipeng Li and DeLiang Wang. Separation of singing voice from music accompaniment for monaural recordings. Technical report, Ohio State University Columbus United States, 2005.

J. Salamon and E. Gómez. Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6):1759–1770, Aug. 2012.

Shayenne Moura and Marcelo Queiroz. Melody and accompaniment separation using enhanced binary masks. In Proceedings of the 16th Brazilian Symposium on Computer Music, pages 164 – 165, São Paulo, 2017. 17th Brazilian Symposium on Computer Music - SBCM 2019

Kyungyun Lee, Keunwoo Choi, and Juhan Nam. Revisiting singing voice detection: a quantitative review and the future outlook. In 19th Int. Soc. for Music Info. Retrieval Conf., Paris, France, 2018.

Shayenne Moura. Singing voice detection using vggish embeddings. 19th International Society for Music Information Retrieval Conference, Paris, France, 2018.
Publicado
25/09/2019
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GORODSCY, Fábio; FEULO, Guilherme; FIGUEIREDO, Nicolas; ITABORAÍ, Paulo Vitor; BODO, Roberto; BORGES, Rodrigo; MOURA, Shayenne. Computer Music Research Group - IME/USP Report for SBCM 2019. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 17. , 2019, São João del-Rei. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 189-191. DOI: https://doi.org/10.5753/sbcm.2019.10443.