Ensemble size classification in Colombian Andean string music recordings 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.