Computer Science ›› 2014, Vol. 41 ›› Issue (2): 174-178.

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Tags Know You Better:A New Approach to Enhancing MIR System

ZHOU Li-juan,LIN Hong-fei and YAN Jun   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Music sharing systems with collaboratively tagging function have been important parts on the Internet.They make the system users to annotate and categorize their own interests and thoughts about the resources possible.In the paper,a novel and straightforward way was proposed to search music collections using metadata and descriptions (tags) of tracks,by jointly considering lyrics,tags and popularity of songs to enhance Music Information Retrieval (MIR) system.Furthermore,Tag Latent Dirichlet Allocation (TLDA) model was proposed in the paper to facilitate adjusted VSM by obtaining more semantically related tags.TLDA can better analyze collaboratively generated tags and understand the intent of user queries in a semantic way,acquiring more information than just keyword-matched tracks return list.By comparing the performance of the proposed approach with general tag clustering approach,a result was found that music information retrieval model proposed in the article performs better than conventional metadata-based music retrieval techniques and tags clustering,especially when tags for tracks are extremely sparse and informal.

Key words: Music information retrieval,Music vector space model,Tag clustering,Tag recommendation,Tag latent dirichlet allocation model

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