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Large music collections afford the listener flexibility in the form of choice, which enables the listener to choose the appropriate piece of music to enhance or complement their listening scenario on-demand. However, bundled with such a large music collection is the daunting task of manually searching through each entry in the collection to find the appropriate song required by the listener. This often leaves the listener frustrated when trying to select songs from a large music collection. In this paper, an overview of existing methods for automatically generating a playlist is discussed. This discussion outlines advantages and disadvantages associated with such implementations. The paper then hightlights the need for contextual and environmental information, which ultimately defines the listener's listening scenario. Environmental features, such as location, activity, temperature, lighting and weather have great potential as meta-data. Here, the key processes of a basic system are outlined, in which the extracted music features and captured contextual data are analysed to create a personalised automatic playlist generator for large music collections.
Reynolds, G., Barry, D., Burke, T., Coyle, E.: Towards a personal automatic music playlist generation alogorithm: the need for contextual information. Proceedings of the 2nd. Audio Mostly Conference: interaction with sound, Fraunhofer Institute for Digital Media Technology, Limenau, Germany, 2007 pp. 84-89.