Grollmisch, S.S.GrollmischCano, E.E.CanoMora Ángel, F.F.Mora ÁngelLópez Gil, G.G.López Gil2022-03-142024-04-152022-03-142021https://publica.fraunhofer.de/handle/publica/41147210.1007/978-3-030-70210-6_4Reliable 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.enAutomatic Music Analysis621006conference paper