Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

Abstract

This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music based on its reviews on websites. The biggest challenge lies in the complicated relations of music styles. It has brought failure to many multi-label classification methods. To tackle this problem, we propose a novel deep learning approach to automatically learn and exploit style correlations. The proposed method consists of two parts: a label-graph based neural network, and a soft training mechanism with correlation-based continuous label representation. Experimental results show that our approach achieves large improvements over the baselines on the proposed dataset. Especially, the micro F1 is improved from 53.9 to 64.5, and the one-error is reduced from 30.5 to 22.6. Furthermore, the visualized analysis shows that our approach performs well in capturing style correlations.

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• Distilling the Knowledge in a Neural Network
• Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features
• Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
• Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method
• Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency