MediaEval 2019: emotion and theme recognition in music using Jamendo

Bogdanov, Dmitry; Porter, Alastair; Tovstogan, Philip; Won, Minz

MediaEval 2019: emotion and theme recognition in music using Jamendo

License

Citation

Bogdanov D, Porter A, Tovstogan P, Won M. MediaEval 2019: emotion and theme recognition in music using Jamendo. In: Larson M, Hicks S, Constantin MG, Bischke B, Porter A, Zhao P, Lux M, Cabrera Quiros L, Calandre J, Jones G, editors. MediaEval’19, Multimedia Benchmark Workshop; 2019 Oct 27-30, Sophia Antipolis, France. Aachen: CEUR; 2019. [3 p.]

Abstract

This paper provides an overview of the Emotion and Theme recognition in Music task organized as part of the MediaEval 2019 Benchmarking Initiative for Multimedia Evaluation. The goal of this task is to automatically recognize the emotions and themes conveyed in a music recording by means of audio analysis. We provide a large dataset of audio and labels that the participants can use to train and evaluate their systems. We also provide a baseline solution that utilizes VGG-ish architecture. This overview paper presents the task challenges, the employed ground-truth information and dataset, and the evaluation methodology.