{"id":1573,"date":"2025-08-28T15:04:08","date_gmt":"2025-08-28T18:04:08","guid":{"rendered":"https:\/\/datamining.dc.uba.ar\/datamining\/?p=1573"},"modified":"2025-09-09T11:35:00","modified_gmt":"2025-09-09T14:35:00","slug":"defensa-de-tesis-tomas-marcos-09-09-2025-10-00-hs","status":"publish","type":"post","link":"https:\/\/datamining.dc.uba.ar\/datamining\/2025\/08\/28\/defensa-de-tesis-tomas-marcos-09-09-2025-10-00-hs\/","title":{"rendered":"Defensa de Tesis \u2013 Tom\u00e1s Marcos \u2013 09\/09\/2025 \u2013 10.00 hs."},"content":{"rendered":"

El Martes 09\/09 se realiz\u00f3 la Defensa de Tesis de Tom\u00e1s Marcos titulada \u201cSistemas de transcripci\u00f3n autom\u00e1tica de bater\u00eda: dise\u00f1o y evaluaci\u00f3n de enfoques basados en aprendizaje profundo moderno y aprendizaje autom\u00e1tico tradicional\u201d.\u00a0 Cont\u00f3 con la direcci\u00f3n del Dr. Pablo Riera. Los jurados fueron el Dr. Hern\u00e1n Bocaccio, la Dra. Laura de la Fuente de la Torre y la Dr. Facundo Quiroga. La defensa puede verse a trav\u00e9s del siguiente link: https:\/\/www.youtube.com\/live\/4g–cMl0mzw<\/a><\/p>\n

Resumen:<\/p>\n

Los sistemas de transcripci\u00f3n autom\u00e1tica de bater\u00eda (TAB) intentan generar una transcripci\u00f3n de los eventos ocurridos en un sistema de bater\u00eda a partir de una pista o canci\u00f3n, lo cual resulta una tarea desafiante. En este trabajo exploraremos diferentes enfoques para integrar un sistema de TAB: modelos o algoritmos de reconocimiento del instrumento, de detecci\u00f3n de eventos y de separaci\u00f3n de fuentes. Se har\u00e1 \u00e9nfasis en los modelos de reconocimiento, probando as\u00ed diferentes t\u00e9cnicas. Algunas de estas tradicionales como m\u00e1quinas de soporte vectorial; otras en cambio, basadas en aprendizaje profundo como son las redes convolucionales (CNN) tanto con caracter\u00edsticas basadas en espectrogramas como con caracter \u0301\u0131sticas basadas en otros modelos como Music undERstanding model with large-scale self-supervised Training (MERT). Por \u00faltimo, se hace prueba de enfoques basados en prompts de lenguaje natural tales como Contrastive Language-Audio Pretraining (CLAP.)<\/p>\n

En principio, se utilizan las anotaciones de verdad de campo (groundtruth) para los modelos de reconocimiento y posteriormente se incorporan m \u0301etodos de detecci\u00f3n de eventos para prescindir de estas, logrando as\u00ed un sistema de TAB utilizando pistas de bater\u00eda aisladas. Como prueba final, se incorporan las pistas con todos los instrumentos y se prueba entrenar modelos con estas pistas completas por un lado. Por otro, se entrenan estos modelos de detecci\u00f3n de eventos y reconocimiento del instrumento pero utilizando un modelo de separaci\u00f3n de fuentes como parte del preprocesamiento (Hybrid-Demucs). Este \u00faltimo enfoque resulta tener mayor \u00e9xito que el primero, principalmente debido a una mejora en el modelo de detecci\u00f3n de eventos. Realizamos las evaluaciones de dichos m\u00e9todos utilizando el conjunto de datos llamado \u201cMDB Drums\u201d. Este mismo, tiene la ventaja de poseer tanto pistas completas como aisladas por tipo de instrumento, lo cual nos permiti\u00f3 evaluar la utilidad de un algoritmo de separaci\u00f3n de fuentes para la tarea de TAB. En la comparativa, notamos que los m\u00e9todos basados en CNN resultan mejores para el reconocimiento del instrumento a lo largo de los instrumentos que integran la bater\u00eda.<\/p>\n

En el agregado, se encuentra un alto rendimiento (medido por F1-score) para los instrumentos de bombo, HiHat y redoblante; mientras que por otro lado se encuentra uno bajo para los instrumentos de pandereta\/side stick, toms y platillos, siendo una de las causas de esto, el tama\u00f1o muestral del conjunto de entrenamiento. Se encuentra que los resultados no var\u00edan significativamente al incorporar un m\u00e9todo de detecci\u00f3n de eventos. Por \u00e9ltimo, se proponen alternativas a futuro para incrementar la calidad del reconocimiento en los instrumentos donde esta no result\u00f3 favorable, tales como incorporar t\u00e9cnicas de data augmentation.<\/p>\n","protected":false},"excerpt":{"rendered":"

El Martes 09\/09 se realiz\u00f3 la Defensa de Tesis de […]<\/p>\n","protected":false},"author":1,"featured_media":49,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1573","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria"],"yoast_head":"\nDefensa de Tesis \u2013 Tom\u00e1s Marcos \u2013 09\/09\/2025 \u2013 10.00 hs. - Maestr\u00eda en Data Mining<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datamining.dc.uba.ar\/datamining\/2025\/08\/28\/defensa-de-tesis-tomas-marcos-09-09-2025-10-00-hs\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Defensa de Tesis \u2013 Tom\u00e1s Marcos \u2013 09\/09\/2025 \u2013 10.00 hs. - 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