An intelligent music playlist generator based on the time parameter with artificial neural networks

Abstract

A music hobbyist listens to different types of music at different times of the day. Thus, an automatic music playlist generator that can adjust to the hobbyist’s daily activities on this basis is necessary in order to generate the appropriate music to suit the user’s current activity, whether it is working or driving. Although existing research has introduced various music playlist generators, there is yet a system that generates the music playlist based on time. Hence, in this paper, we present a music playlist generation system, which provides an automatic and personalized music playing service based on the time parameter. This system represents the characteristics of music from features extracted out of both the music’s symbolic form and wave data. The kernel of this system is based on a modified artificial neural network. The user’s music rating history and the associated time stamps in the user’s profile constitute the training data of the modified artificial neural networks. A collaborative method has also been proposed to reduce the effect of the cold start problem upon system initialization. A series of experiments have been carried out to demonstrate the performance of this system.