![]() This would allow recommendation of similar music by selecting and sequencing songs according to their Euclidean distance from the user's coordinates, within a defined radius. In practice, users of the system could self-report their emotional state as coordinates in arousal and valence space. The aim of this research is to develop a system that can organise the vast amount of music that is available to the general public in a meaningful, personalised way, by providing a mechanism by which music can be emotionally labelled. To this end, a scalable solution for designing affective music playlists is proposed in this paper, which has the ability to account for such emotional factors. In correspondence with this, the use of emotion in creating music playlists is a frequently occurring concept with users (Stumpf & Muscroft, 2011), yet currently there are few effective systems for organising music according to emotion using computational methods. Studies have suggested that the most common motive for listening to music is to influence emotions (Juslin & Sloboda, 2011). Music is often regarded as a language of emotions (Cooke, 1990) it expresses feelings that listeners perceive, recognise, and are moved by. Affective music playlists have recently begun to receive more widespread attention as an alternative approach, which takes these emotional concepts into account. More importantly, with respect to affective computing, these systems do not make recommendations based upon an individual's emotional state or incorporate knowledge of the emotional content and perceptual effects of the music. Content-based methods also lack an understanding of users' interests and preferences (Shao et al., 2009). Collaborative filtering methods require a large amount of historical data from users, which gives rise to issues of privacy. While these approaches provide one possible solution for generating music playlists, they have several disadvantages. demographics, preferences, etc.) and features they are interested in (musical genre, tempo, artist, etc.) (Mobasher et al., 2000). The latter also uses a rating system, but focusing upon the attributes of users (e.g. The former is a community-based process that typically uses a rating system, which bases its recommendations upon other users that have similar tastes (Ricci et al., 2011). The approaches that are employed by such systems often draw upon collaborative or content-based filtering techniques, which have their digital origins in recommendation systems for online shopping and other commercial applications. ![]() Music recommendation systems have become commonplace on platforms that support the organisation and acquisition of digital music. This has led to a revolution in the way music is used in everyday life, where music playlists can be easily created to reflect a host of situations, intentions and contexts (Juslin & Laukka, 2004 Kamalzadeh et al., 2012). The choice and portability of music afforded by the digital revolution has actually made listeners more active as opposed to passive, in that they seek out particular songs for specific emotional or contextual purposes, rather than being exposed to the playback of music over which they have little or no control in the selection of North et al. ( 2004) and Krause et al. ( 2015). The ability to carry a large digital library of music is now commonplace, thanks to the advent of psychoacoustic audio compression techniques and portable digital music players, the function of which have increasingly become integrated with ubiquitous devices, such as laptops, tablets and smartphones.
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