  We present an efficient architecture to detect mood/themes in music tracks on
autotagging-moodtheme subset of the MTG-Jamendo dataset. Our approach consists
of two blocks, a CNN block based on MobileNetV2 architecture and a
self-attention block from Transformer architecture to capture long term
temporal characteristics. We show that our proposed model produces a
significant improvement over the baseline model. Our model (team name: AMLAG)
achieves 4th place on PR-AUC-macro Leaderboard in MediaEval 2019: Emotion and
Theme Recognition in Music Using Jamendo.

Sound;Machine Learning;Audio and Speech Processing