Abstract
Neuroevolution techniques combine genetic algorithms with artificial neural networks, some of them evolving network topology along with the network weights. One of these latter techniques is the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. For this pilot study we devised an extended variant (joint NEAT, J-NEAT), introducing dynamic cooperative co-evolution, and applied it to sound event detection in real life audio (Task 3) in the DCASE 2017 challenge. Our research question was whether small networks could be evolved that would be able to compete with the much larger networks now typical for classification and detection tasks. We used the wavelet-based deep scattering transform and k-means clustering across the resulting scales (not across samples) to provide J-NEAT with a compact representation of the acoustic input. The results show that for the development data set J-NEAT was capable of evolving small networks that match the performance of the baseline system in terms of the segment-based error metrics, while exhibiting a substantially better event-related error rate. In the challenge, J-NEAT took first place overall according to the F1 error metric with an F1 of 44:9% and achieved rank 15 out of 34 on the ER error metric with a value of 0:891. We discuss the question of evolving versus learning for supervised tasks.