Print Email Facebook Twitter On detecting the playing/non-playing activity of musicians in symphonic music videos Title On detecting the playing/non-playing activity of musicians in symphonic music videos Author Bazzica, A. (TU Delft Multimedia Computing) Liem, C.C.S. (TU Delft Multimedia Computing) Hanjalic, A. (TU Delft Multimedia Computing) Date 2016 Abstract Information on whether a musician in a large symphonic orchestra plays her instrument at a given time stamp or not is valuable for a wide variety of applications aiming at mimicking and enriching the classical music concert experience on modern multimedia platforms. In this work, we propose a novel method for generating playing/non-playing labels per musician over time by efficiently and effectively combining an automatic analysis of the video recording of a symphonic concert and human annotation. In this way, we address the inherent deficiencies of traditional audio-only approaches in the case of large ensembles, as well as those of standard human action recognition methods based on visual models. The potential of our approach is demonstrated on two representative concert videos (about 7 hours of content) using a synchronized symbolic music score as ground truth. In order to identify the open challenges and the limitations of the proposed method, we carry out a detailed investigation of how different modules of the system affect the overall performance. To reference this document use: http://resolver.tudelft.nl/uuid:df2706c1-1555-4cf5-a47a-d21251bec281 DOI https://doi.org/10.1016/j.cviu.2015.09.009 ISSN 1077-3142 Source Computer Vision and Image Understanding, 144, 188-204 Bibliographical note harvest http://mmc.tudelft.nl/content/detecting-playingnon-playing-activity-musicians-symphonic-music-videos Part of collection Institutional Repository Document type journal article Rights © 2016 A. Bazzica, C.C.S. Liem, A. Hanjalic Files PDF 1_s2.0_S1077314215002040_main.pdf 5.62 MB Close viewer /islandora/object/uuid:df2706c1-1555-4cf5-a47a-d21251bec281/datastream/OBJ/view