Efficient Fault Tolerant Repeating Pattern Extractions in Music Databases

• Main Authors: Chih-Chuan Chung, 鍾智全
• Other Authors: Yu-lung Lo
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/15728212776967471851

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 95 === In the research of music databases, a repeating pattern is an important feature of music objects, which commonly used in analyzing the repeated part of music data and looking for theme. Most of repeating patterns are key melodies or easy to familiarize and remember for people. Non-trivial repeating patterns exclude those repeating patterns included in other longer patterns such that they can reduce the redundancy and speedup music search. So far, existing approaches discover a repeating pattern in such a way that the sequence of notes in a music object appears more than once in exactly matching. If we allow the similar sequences with partial different notes to be a fault tolerant repeating pattern, it can reduce the number of repeating patterns and can construct more efficient music indexes. Furthermore, the number of fault tolerant non-trivial repeating patterns can still be a large amount such that some researches claimed that the longest repeating patterns that can be characterized as an important feature of melody strings. Therefore, if we can discover the longest fault tolerant repeating pattern in a music object, it can be more suitable to represent the theme of the music. In the existing approaches, they are time consuming for finding fault tolerance non-trivial repeating patterns in a music objects and none of them discusses the discovering of the longest fault tolerance repeating patterns. In this paper, we aim to develop efficient approaches to discover fault tolerance non-trivial repeating patterns in a music object as well as to find the longest repeating pattern. We expect that the applications of our approach can be extended such as applying to the searching for significant repeating patterns on the DNA or protein gene sequences.