Authors: Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}.
dataset using the real mixtures they provide. categories on the MUSIC dataset. If we train the network from scratch on Solos, results improve by almost 1 dB. However, it is possible to achieve an even better result fine-tuning the network, pre-trained with MUSIC, on Solos. We hypothesize that the improvement occurs as the network is exposed to much more training data. Moreover, the table results show how it is possible to reach higher performance by using more powerful architectures like MHU-Net.
dataset using the real mixtures they provide.