Study on Categorization of Alpha Wave Music

• Main Authors: Zih-Yin Lai, 賴姿吟
• Other Authors: Yu-Lung Lo
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/55618494978838890600

碩士 === 朝陽科技大學 === 資訊管理系 === 103 === Since portable devices and digital audio players (ex. iPad, iPod, iPhone, Smartphone, etc.) become more and more popular, the essential of digital music also becomes urgent. It brings on the applications of music database in great demand. The content of digital music provides many features which can be used for music analysis and retrieval. The music features, such as melody, rhythm, chord, and so on, can represent the music styles and characteristics. Therefore, content-based music retrieval is an important research field for music databases. The related researches consist of music classification, music feature extraction, music indexing, approximate music searching and so forth which are all used for users to easily and quickly search the target in a music database. Furthermore, music therapy uses music to help patients to improve or maintain their physical and spiritual health. When people relax with closed eyes, an alpha wave in the frequency range of 8–12Hz appears with brain signals. There were many medical reports proofed that some specific music can resonate with the alpha wave. Therefore, these alpha wave music can improve more relaxing for people and is very helpful when they need to take a rest. That's why people like to listen music when relaxing. Currently, these of specific music are classified manually by expertise only. The existing music classification approaches are almost all categorized by styles and genres, such as pop, classical, jazz, folk, etc. Accordingly, till now, there is no research report studied about classification of alpha wave music. In this research, we will investigate the content-based features of the alpha wave music, and develop the classification method for alpha wave music. We expect our effort can help to expand the applications and develop the more realistic of music classification system as well as to aid music therapy.