| Summary: | 碩士 === 國立臺灣大學 === 電信工程學研究所 === 97 === During the past few decades, the world has ushered in a new era, with booming Internet technology and immense multimedia content distribution. The acquisition and circulation of digital music ‾le become much easier than ever. Due to this rapidly rising of music quantity, a brand new way of discovering and recommending music is thus highly expected.
In the beginning of this study, a conventional music similarity measure system based on the signal analysis methods is implemented and evaluated. According to the experimental results, it shows that the low-level features from signal analysis techniques are not strong enough to ful‾ll the discrimination between various musical content, such as the chord progression, genre, instrumentation, and melody. Therefore, the aim of this study is to incorporate the low-level feature with the mid-level feature, in order to utilize the musical content. We focus on the way to extract the instrumentation information leaved by the composers. The time-frequency analysis of musical instrumental signals and the classification problem of various instruments in the monophonic case are studied. After that, we extend the idea to deal with the polyphonic music and analyze its time-varying instrumentation information. By incorporating this information back to the original similarity measure system, the calculated similar songs can resemble to each other specifically in the sense of the instrumentation.
|
|---|
