A Method of Music Auto-tagging Based on Audio and Lyric

Abstract

With the development of the Internet and technology online music platforms and music streaming services are booming the large number of digital music makes users face the problem of information overloading In order to solve this problem these platforms need to construct a comprehensive recommendation system by using user information and meta data to help users in searching querying or discovering new music Social tags are considered to help the music recommendation system to make better recommendations However social tags face the problem of tag sparsity and cold start limiting their effectiveness in helping the recommendation system To solve these problems it is necessary to supplement the shortage of the tags through a music auto-tagging system In the past most of the research on auto-tagging used only audio for analysis However many studies have proved that the lyrics can help the music classification system to obtain more information and improve the classification accuracy This study proposed a method of music auto-tagging which use both audio and lyric for analysis Besides we also experimented the different architecture of tag classification the result shows that the structure using late fusion model and multi-task classification method has the best performance

Date of Award	2019
Original language	English
Supervisor	Hei-Chia Wang (Supervisor)