The effect of music listening maps on second grade music students' preference for and understanding of orchestral music

This study investigated the use of music listening maps to help learning and the preferences of second graders for orchestral music. Subjects were a population of four 2nd grade classes, and were randomly divided into two groups. The investigation was a counterbalanced, post-test only design, lasting for three consecutive classes. Two treatments/lessons were presented and a third lesson was a review. In Treatment 1 Group I used listening maps first, while Group II received instruction without listening maps. In Treatment 2, the order was reversed. Two post-tests and a comprehensive test were administered. An affective survey was administered after the treatments, measuring student preference and attitude. When listening maps were presented, scores were significantly higher. It did not matter whether the listening maps were presented first or not. Results of the survey show student preference will increase with music listening maps.

User-centric music information retrieval

The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for. Traditionally, songs were organized in a hierarchical structure such as genre->artist->album->track, to facilitate the users’ navigation. However, the intentions of the users are often hard to be captured in such a simply organized structure. The users may want to listen to music of a particular mood, style or topic; and/or any songs similar to some given music samples. This motivated us to work on user-centric music retrieval system to improve users’ satisfaction with the system. The traditional music information retrieval research was mainly concerned with classification, clustering, identification, and similarity search of acoustic data of music by way of feature extraction algorithms and machine learning techniques. More recently the music information retrieval research has focused on utilizing other types of data, such as lyrics, user-access patterns, and user-defined tags, and on targeting non-genre categories for classification, such as mood labels and styles. This dissertation focused on investigating and developing effective data mining techniques for (1) organizing and annotating music data with styles, moods and user-assigned tags; (2) performing effective analysis of music data with features from diverse information sources; and (3) recommending music songs to the users utilizing both content features and user access patterns.