Salsa dataset: first salsa music knowledge base
Main Article Content
Keywords
Salsa Musical Genre, Dataset, Music Information Retrieval
Abstract
Salsa is a well-known musical genre and part of our cultural identity. Its origins date back to the 30s of the last century and it has grown in popularity since its origins. Ever since, by different artists in different regions of the world have modified the genre, through different visions of what salsa is, experimenting and adding new instruments and technology. Thus, salsa becomes an intrinsically complex and difficult genre in qualitative terms. However, little we know at a computational level, about which are the acoustic characteristics defining this music, to make it different from the rest of musical genres. In this paper we show the results of a process that builds a knowledge base of salsa music freely available to the scientific community. This base gathers acoustic information of over 20.000 salsa songs. We pretend to use this information to model different characteristics of the genre by means of AI techniques. In addition to making information accessible to researchers interested in salsa music, another important contribution of this project is to provide tools to enlarge the knowledge base with the help of the scientific community. To achieve this, we developed a software that extracts the pertinent acoustic information from songs belonging to users and then it includes them into the knowledge base.
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References
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Mitrović,¬D.,¬ Zeppelzauer,¬M.¬ &¬ Breiteneder,¬C.¬(2010). Features for content¬based audio retrieval. Advances in Computers, 78, 71¬15 0 .
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Padilla,¬ F.¬ M.¬ (1989).¬ Salsa¬ music¬ as¬ a¬ cultural¬expression¬of¬ Latino¬ consciousness¬and¬ unity.-Hispanic Journal of Behavioral Sciences, 11(1), 28¬45.
Pampalk,¬E.¬ (2006).¬Computational models of music similarity and their application in music information retrieval. Viena: Technische Universitat Wien, disertación doctoral.
Peeters,¬G.¬ (2007).¬ Sequence¬representation¬of¬music¬ structure¬using¬ higher ¬order¬ similarity-matrix¬and¬ maximum¬likelihood¬approach.¬
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Schnitzer,¬D.,¬ Flexer,¬ A.,¬ &¬ Widmer,¬G.¬ (2009).¬ A¬filter ¬and ¬refine¬ indexing¬method¬ for¬ fast¬ similarity¬search in millions of music tracks. En Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009), Kobe, Japan.
Silla Jr., C. N., Koerich, A. L., & Kaestner, C. A. A. (2008). The Latin music database. En Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008),¬Philadelphia,-PA.
Sturm,¬ B.¬ L.¬ (2012).¬ An¬ analysis¬ of¬ the¬ gtzan¬ music¬genre dataset. En 2nd International ACM Workshop on Music Information Retrieval with User-Centered and Multi-modal Strategies (MIRUM2012), held as part of the ACM Mulitmedia 2012, Nara, Japón.
The¬ HDF¬ Group¬ (2016).¬Welcome to the HDF5 home page! Recuperado el ... de ... de ..., de: https://www.hdfgroup.org/HDF5/
Tzanetakis,¬G.¬ &¬ Cook,¬ P.¬ (2002).¬ Musical¬ genre¬classification¬of¬audio ¬signals. ¬IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.
Wold,¬ E.,¬ Blum,¬ T.,¬ Keislar,¬ D.,¬ &¬ Wheaton,¬J.¬ (1996).¬Content ¬based¬ classification,¬search,¬ and¬ retrieval¬of¬audio. IEEE Multimedia, 3(3), 27¬36.
Xiao,¬ L.,¬ Tian,¬ A.,¬ Li,¬ W.,¬ &¬ Zhou,¬ J.¬ (2008).¬ Using¬ a¬stochastic model to capture the association between timbre and perceived tempo. En Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008),¬Philadelphia,¬PA
Aucouturier,¬J. ¬J.,¬ &¬ Pachet,¬ F.¬ (2003).¬ Representing¬musical genre: a state of the art. Journal of New Music Research, 32(1), 83¬93.
Bertin ¬Maheux,¬T.,¬ Ellis,¬ D.¬ P.¬ W.,¬ Whitman,¬B., ¬&¬ Lamere,¬ P.¬ (2011).¬ The¬ million¬ song¬ dataset.¬ En -Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR2011), Miami, FL.
Cios,¬ K.¬ J.,¬ Pedrycz,¬ W.,¬ Swinarski,¬R.¬ W.,¬ &¬ Kurgan,¬L. A. (2007). Data mining: a knowledge discovery approach. Nueva York: Springer.
Cooper, M., & Foote, J. (2003). Summarizing popular music via structural similarity analysis. En IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.¬New¬Paltz,¬NY.
Duany,¬ J.¬ (1984).¬ Popular¬ music¬ in¬ Puerto¬ Rico:¬toward an anthropology of “salsa”. Revista de Música Latino Americana, 5(2), 186¬216.
Echonest (2016). Echonest. Recuperado el ... de ... de ..., de: http://developer.echonest.com
Fitch, W. T. (2005). The evolution of music in comparative perspective. Annals of the New York Academy of Sciences, 1060(1), 29¬49.
Holmes,¬ G.,¬ Donkin,¬ A.,¬ &¬ Witten,¬ I.¬ H.¬ (1994).¬Weka: a machine learning workbench. Hamilton, New¬ Zealand:¬University¬of¬ Waikato,¬Department¬of¬Computer Science, working paper 94/09.
Isaacson, E. (2005). What you see is what you get: on visualizing music. En Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005). Londres, UK.
Ismir (2004). ISMIR2004 audio description contest-genre/artist ID classification and artist similarity. Recuperado el ... de ... de ..., de: h t t p : // i s m i r 2 0 0 4 .ismir.net/genre_contest/
Jungermann,¬F.¬ (2009).¬ Information¬extraction¬with rapidminer. En Proceedings of the GSCL Symposium ‘Sprachtechnologie und eHumanities’. Essen, Alemania.
Li, T., & Li, L. (2012). Music data mining: an introduction. En Li, T., Ogihara, M., & Tzanetakis, G.¬ (ed.).¬Music data mining, pp. ...¬... Nueva York: CRC Press
Li,¬ T.,¬ Ogihara,¬M.,¬ &¬ Zhu,¬ S.¬ (2006).¬ Integrating¬features from different sources for music information retrieval. En Proceedings of the 6th International Conference on Data Mining, Hong Kong, China.
McDermott, J. (2008). The evolution of music. Nature, 453(7193), 287¬288.
McKay, C., McEnnis, D., & Fujinaga, I. (2006). A large publicly accessible prototype audio database for music research. En 7th International Conference on Music Information Retrieval (ISMIR2006), Victoria, Canadá.
Mitrović,¬D.,¬ Zeppelzauer,¬M.¬ &¬ Breiteneder,¬C.¬(2010). Features for content¬based audio retrieval. Advances in Computers, 78, 71¬15 0 .
Mora, M. J. (2013). Recuperación basada en contenido de archivos de sonido Mpeg-7 sobre Oracle multimedia.¬ Cali:¬ Pontificia¬Universidad¬Javeriana,¬trabajo de grado de maestría.
Morales, E. (2003). The latin beat: the rhythms and roots of latin music from bossa nova to salsa and beyond.¬Boston,¬MA:¬Da¬Capo¬Press.
Orio, N. (2006). Music retrieval: a tutorial and rev iew. Foundations and Trends in Information Retrieval, 1(1), 1¬90.
Orio, N., Rizo, D., Miotto, R., Montecchio, N., Schedl, M. & Lartillot, O. (2011). Musiclef: a benchmark activity in multimodal music information retrieval. 7171Salsa Dataset: Primera base de conocimiento de música salsaGerardo M. Sarria M. - Mario Julián Mora - Carlos Arce-Lopera En Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR2011), Miami, FL.
Padilla,¬ F.¬ M.¬ (1989).¬ Salsa¬ music¬ as¬ a¬ cultural¬expression¬of¬ Latino¬ consciousness¬and¬ unity.-Hispanic Journal of Behavioral Sciences, 11(1), 28¬45.
Pampalk,¬E.¬ (2006).¬Computational models of music similarity and their application in music information retrieval. Viena: Technische Universitat Wien, disertación doctoral.
Peeters,¬G.¬ (2007).¬ Sequence¬representation¬of¬music¬ structure¬using¬ higher ¬order¬ similarity-matrix¬and¬ maximum¬likelihood¬approach.¬
En¬Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Viena,Proud¬ Music¬ (2016).¬Proud Music. Recuperado el ... de ... de ..., de: http://www.proudmusiclibrary.com/en/tag/salsa/Rauber,¬ A.,¬ Pampalk,¬E.,¬ &¬ Merkl,¬ D.¬ (2002).¬Content ¬based¬ music¬ indexing¬and¬ organization.¬En¬Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 409¬410. NuevaYork: ACM.
Schnitzer,¬D.,¬ Flexer,¬ A.,¬ &¬ Widmer,¬G.¬ (2009).¬ A¬filter ¬and ¬refine¬ indexing¬method¬ for¬ fast¬ similarity¬search in millions of music tracks. En Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009), Kobe, Japan.
Silla Jr., C. N., Koerich, A. L., & Kaestner, C. A. A. (2008). The Latin music database. En Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008),¬Philadelphia,-PA.
Sturm,¬ B.¬ L.¬ (2012).¬ An¬ analysis¬ of¬ the¬ gtzan¬ music¬genre dataset. En 2nd International ACM Workshop on Music Information Retrieval with User-Centered and Multi-modal Strategies (MIRUM2012), held as part of the ACM Mulitmedia 2012, Nara, Japón.
The¬ HDF¬ Group¬ (2016).¬Welcome to the HDF5 home page! Recuperado el ... de ... de ..., de: https://www.hdfgroup.org/HDF5/
Tzanetakis,¬G.¬ &¬ Cook,¬ P.¬ (2002).¬ Musical¬ genre¬classification¬of¬audio ¬signals. ¬IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.
Wold,¬ E.,¬ Blum,¬ T.,¬ Keislar,¬ D.,¬ &¬ Wheaton,¬J.¬ (1996).¬Content ¬based¬ classification,¬search,¬ and¬ retrieval¬of¬audio. IEEE Multimedia, 3(3), 27¬36.
Xiao,¬ L.,¬ Tian,¬ A.,¬ Li,¬ W.,¬ &¬ Zhou,¬ J.¬ (2008).¬ Using¬ a¬stochastic model to capture the association between timbre and perceived tempo. En Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008),¬Philadelphia,¬PA
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Published
Dec 12, 2016
Article Details
How to Cite
Sarria M., G. M., Mora, M. J., & Arce-Lopera, C. (2016). Salsa dataset: first salsa music knowledge base. Ricercare, (5), 63–72. https://doi.org/10.17230/ricercare.2016.5.5
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Author Biographies
Gerardo M. Sarria M., Pontificia Universidad Javeriana, Cali
Doctor of Engineering (Computer Science) from Universidad del Valle, Cali. Associate Professor of the Pontificia Universidad Javeriana, Cali
Mario Julián Mora, Universidad San Buenaventura, Cali
Executive Master in Technological Innovation of the EOI Business School in Madrid, Spain. Dean of the Faculty of Engineering of the San Buenaventura University, Cali
Carlos Arce-Lopera, Universidad ICESI, Cali
PhD in Engineering from Yokohama National University of Japan. Assistant Professor at ICESI University, Cali
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