'Salsa', a Dataset for Beat Estimation in Salsa Music - Daniel Gómez-Marín; Rafael Ospina-Caicedo; Javier Díaz-Cely; Jesús Paz; Sergi Jordà; Perfecto Herrera (Transactions of the International Society for Music Information Retrieval)
This paper introduces a dataset of salsa music, a genre deeply rooted in Latin American culture that is known for its intricate yet captivating rhythms, which pose challenges even for seasoned dancers. Our work involved creating a comprehensive track selection and meticulously annotating beat occurrences. The dataset comprises 124 expert-analyzed salsa songs, offering a rich resource for further beat estimation and related studies within the salsa music domain. We detail the dataset, outline the methodology carried out for compiling and validating beat annotations, and finally test two contemporary beat prediction models on the dataset. Our contributions include the establishment of a labeled dataset for beat estimation research in salsa music and a robust methodology for identifying beat occurrences. Through this work, we aspire to enrich contemporary and future studies on Latin American culture, particularly the integral aspect of salsa music, fostering rhythm analysis and other musical properties that can derive from it.