Applications of deep neural networks on music emotion recognition

Abstract

Music Emotion Recognition (MER) is a subfield of Music Information Retrieval (MIR) that focuses on finding a relationship between music and human emotions by applying machine learning and signal processing techniques. In recent years, neural networks have achieved great success in a large number of areas, such as speech recognition and image processing, sparking many attempts to utilize neural networks in the MER task. Although new models for MER are constantly emerging, there are few systematic reviews in this field that involves the latest models and datasets. Therefore, in this paper, we provide a detailed review of this task. Our work first expounds on the practical significance and research status of the MER problem. Then, we encapsulate the background research and contributions of predecessors in both machine learning and psychology fields. Our work also includes a thorough analysis of several important datasets and their mathematical principles. Finally, we summarize four novel models from an application perspective and conclude a few potential challenges for the task.