Content And User History-Based Music Visual Analysis

Music plays a key role in our daily life. With the rapid development of internet and the great improvement of store technology, now people have got incredible ability to collect and store music. However, their ability to deal with music does not scale with the music library. To address this problem, several approaches have been proposed to analyze music. One method is tag-based, which analyzes and filters music by artist, album, genre and other tag information. The second approach is content-based. According to music content, several features can be extracted and used to analyze the similarity between music, such as timbre, chroma and rhythm. With the content-based method, people can easily find out the rules, relations and patterns hidden in music features. Machine learning is another music analysis approach. Through learning the unknowns in samples, all the music can be divided into several groups. In a word, all these approaches are based on music content without taking account users'preference and behavior. To deal with this problem, several researchers proposed the content and mood based solution. They use both content and people's mood to analyze music, and try to make everyone satisfied. However, only several kinds of mood are not enough. In addition, how to describe music and how to illustrative the relative relationship is another unsolved problem. Though some researchers proposed visualization-based solution, their methods still have some limitations, and can't reveal more details.This thesis first surveys the existing work on music analysis. Based on this survey, we focus on several key problems and propose content and user history based music visual analysis. The innovation and contribution of this thesis mainly include:1. Based on music information retrieval, this thesis first extracts three music features-Timbre, Chroma and Rhythm as the basis of our work. These features include some redundant and unnecessary information, which affects the analysis efficiency. To deal with this problem, two visualization-based music feature optimization approaches are proposed. An improved parallel coordinates is used to eliminate the unnecessary information, while the extending scatterplots technique is used to explore the redundant feature. The experiment shows that these approaches are feasible and work well. 2. Music recommendation algorithm is a key point in music analysis. However the existing work only focuses on music content without taking account users'preference too much. As a result, these algorithms fail to reveal users' preference. To address this problem, a content and user history based music recommendation algorithm is proposed. The collaborative algorithm is used to analyze a user's interaction history, while the similarity algorithm and graph-based algorithm are used to analyze the music features. The experiment indicates that our approach performs better than the traditional algorithm, and recommends suitable music list to different user according to his preference.3. The description of music and music relationship is another focus in music analysis. According to the existing approaches, Overview+Detail and Focus+Contex techniques are usually used to describe the overall information and details. However, these approaches have the disadvantages of music importance and relationship illustration. This thesis proposes a layer-based music information visualization to solve this issue. In this approach, all pieces of music are divided into three layers-most important layer, secondary important layer and auxiliary layer according to the user's preference and similarity between music. With recommendation algorithm and layout technique, we complete the visualization of the first two layers. While with music cloud, we reveal the auxiliary music and corresponding relationship. The user study shows that most of the users are satisfied with the layer-based music information visualization, and think this approach is intuitive. In addition, most of the users are interested in the design of music cloud, and feel it's helpful to make a decision.4. Finally, a prototype system for music visual analysis is designed and implemented based on the proposed algorithms and our work. Especially, several relative techniques are introduced separately as a footnote, such as visual coding, playlist creation and some interaction techniques.