Performance improvement of music mood classification using hyper music features

Abstract

When people want to find music, they traditionally search it with its related symbolic information, such as title, lyrics, and name of the artist. As the digital music database becomes massive, however, it is not effective to rely only on those conventional queries for finding a specific song from the huge music database, because the user often forget the title or name of the artist. Moreover, it is getting common that the users want to be recommended a contextually proper playlist. Therefore, many polished music information retrieval techniques have developed so far, for instance, query by humming or tapping, finding similar songs to the seed songs, recommend songs with specific mood and genre. It is clear that those automated music search systems are heavily based on automatic music classification. It is almost impossible to manually extract important features and classify them with a database of thousands of songs, which is relatively small size though. This thesis deeply concerns audio music mood classification (AMC) which plays a key role in one of the most promising next generation music exploring systems. In order to take mood into account for the AMC, we should formulate the vague concept, mood. After that, it is required that reliable mappings between songs and moods based on human assessment. To fulfill the requirement for trustworthy research results, we adapt five mood classes, which were defined and verified in MIREX (Music Information Retrieval Evaluation eXchange). Similarly, we also used 600 mood-labeled music data which MIREX offers and uses for the contest. For the similar reasons, we used MARSYAS for the reference system. MAR-SYAS, the most famous music information retrieval system, contains well-known music features and Support Vector Machine (SVM) classifier. It is a universal system, but it ranked the first and second in the MIREX AMC tasks, respectively. In this thesis, mid-level music features are introduced. To explore the neces...