Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Ensemble Size Classification in Colombian Andean String Music Recordings

: Grollmisch, S.; Cano, E.; Mora Ángel, F.; López Gil, G.


Kronland-Martinet, R.:
Perception, Representations, Image, Sound, Music : 14th International Symposium, CMMR 2019, Marseille, France, October 14-18, 2019, Revised Selected Papers
Cham: Springer Nature, 2021 (Lecture Notes in Computer Science 12631)
ISBN: 978-3-030-70209-0 (Print)
ISBN: 978-3-030-70210-6 (Online)
ISBN: 978-3-030-70211-3
International Symposium on Computer Music Multidisciplinary Research (CMMR) <14, 2019, Marseille>
Fraunhofer IDMT ()

Reliable methods for automatic retrieval of semantic information from large digital music archives can play a critical role in musicological research and musical heritage preservation. With the advancement of machine learning techniques, new possibilities for information retrieval in scenarios where ground-truth data is scarce are now available. This work investigates the problem of ensemble size classification in music recordings. For this purpose, a new dataset of Colombian Andean string music was compiled and annotated by musicological experts. Different neural network architectures, as well as pre-processing steps and data augmentation techniques were systematically evaluated and optimized. The best deep neural network architecture achieved 81.5% file-wise mean class accuracy using only feed forward layers with linear magnitude spectrograms as input representation. This model will serve as a baseline for future research on ensemble size classification.