Music theme recognition using CNN and self-attention

Autor:	Sukhavasi, Manoj, Adapa, Sainath
Rok vydání:	2019
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: MediaEval 2019, 27-29 October 2019, Sophia Antipolis, France
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.07041 Zobrazit plný text záznamu View this record from Arxiv