Thesis Open Access
Music conveys emotion and is connected to personal memories. Personal memories are linked to social and emotional relations with peers, family and friends. Music memory has been shown in several studies as a spared condition by the neurodegenerative effects of Alzheimer’s disease (AD). Other studies have suggested the enhancing effect of music in autobiographical memory recall in AD. Moreover, music listening is currently a widespread practice in therapeutic sessions for dementia treatment. However, the selection of music is generally manual and lacks of user-oriented approach, being difficult to match patient’s musical taste as a result.
In this work, biographical data from the subjects is combined with musical preferences data in order to generate music playlists made of songs that can be considered part of their life soundtrack. For this purpose, we have built a music database of songs from a specific range of years, based on the age of our target group. The algorithm developed uses metadata generated through questionnaires and searches in last.fm for music that is similar to the preferred songs and artists of the subject. The goal is to generate a final playlist that is close to be the life soundtrack of the subject. This is evaluated by the subjects and its performance is discussed identifying the main challenges for future improvements.
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