  In this paper, we describe our contribution to Task 2 of the DCASE 2018 Audio
Challenge. While it has become ubiquitous to utilize an ensemble of machine
learning methods for classification tasks to obtain better predictive
performance, the majority of ensemble methods combine predictions rather than
learned features. We propose a single-model method that combines learned
high-level features computed from log-scaled mel-spectrograms and raw audio
data. These features are learned separately by two Convolutional Neural
Networks, one for each input type, and then combined by densely connected
layers within a single network. This relatively simple approach along with data
augmentation ranks among the best two percent in the Freesound General-Purpose
Audio Tagging Challenge on Kaggle.
