The music key finding algorithm based on the maximum key-profile correlation

• Main Authors: Sz-Yuan Lee, 李思源
• Other Authors: 許舜斌
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/52089530722847823841

碩士 === 國立中興大學 === 電機工程學系所 === 98 === Abstract In recent years, by the fast development of the Internet and the advancement in the compression techniques, there are large amounts of multimedia data have been rapidly spread on the Internet, the applications of digital multimedia data have been increasing and content-base multimedia analysis has become the focus of recent research. The music key in high level music content is very important. It plays a crucial role in many music classification applications, such as the music style classification [1] and the music mood analysis [2]. Besides, the music key provides a very convenient clue for digital multimedia data searching, and it is also a very useful searching condition for the user and related field researcher. This study reconsiders the Krumhansl’s maximum key-profile correlation algorithm via the viewpoint of statistics, then using key parameter average to train a new weighting table. Finally, this study combines music theory and weighting the last note to reduce the amount of calculation and classification accuracy. Chapter one introduces the study of music key classification. The pioneer of music key classification is Krumhansl’s maximum key-profile correlation algorithm. Afterwards other researchers using Spiral Array Center of Effect, Hidden Markov Model, the chord transient, and the interval of the music to classify music key. For the reading convenience, chapter two introduces some concepts about music theory first, and then states the experiment in [4] about the experimental materials, methods and the procedures. At the end of this chapter, Temperley [5] noted that some unreasonable part of the experiment result and the improvement. Due to the weighting table from Krumhansl [4] and Temperley [5] are artificial, there may exist some arbitrary decisions. In order to avoid such case, this study uses key parameter average to train a new weighting table in chapter three. Chapter four uses the scale structure of music theory to reduce the amount of calculation, and weighting the last note by using minimum mean square error. It applies to the maximum key-profile correlation algorithm to enhance the performance and the practicality. To conclude, this study shows a method to establish the weighting table of maximum key-profile correlation algorithm. It also provides some ways to improve the performance which includes reducing the amount of calculation and raising classification accuracy. The idea can be used to establish a normal weighting table by a large amount database for future research, and combining music style classification to enhance the music key classification performance.