Music Identification Based On Compressed Sensing

With the rapid development of the network technology and the multimedia technology, various kinds of information grow geometrically, and people are also more exposed to the large amount of multimedia content. Audio is one of the most important multimedia. One of the prominent issues is how to automatically manage the content of such audio media. Especially for many types of music, it requires fast and efficient way to identify them in order to find the necessary audio information quickly. This dissertation focused on music identification based on compression sensing algorithm.The focus of this thesis, music identification based on compressed sensing algorithm, which belongs to the field of content-based music identification. It not only saves the manual input work of music's subjective information but also expands the way of music search to identify different music by the rhythm features of music.This thesis first discussed the basic characteristics of music and music identification, and then proposed several rhythm features which can be used with the compressed sensing theory as music identification features. Second, focused on the compressed sensing algorithm in dealing with perceived advantages of multimedia data, and identify areas of the music used in the feasibility. Finally, simulation results verify the feasibility of music identification based on compressed sensing theory, and tested many the different situations of music recognition.