Effective music tagging through advanced statistical modeling

Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this paper, we introduce a novel system called MMTagger that effectively integrates both multimodal and temporal information in the representation of music signal. The carefully designed multilayer architecture of the proposed classification framework seamlessly combines Multiple Gaussian Mixture Models (GM...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Jialie Shen, Wang Meng, Shuichang Yan, HweeHwa Pang, Xiansheng Hua
• Keywords: Information Management, Large Music, Music Information Retrieval, SIGIR 2010, Temporal Information