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- W4386585228 abstract "Music can create, change and lighten one’s mood, and when technologies of artificial intelligence meet with music it can be a great combination. Selecting appropriate songs according to our emotion from such a wide range is a very tedious and timeconsuming task. It takes a lot of time and effort to manually group songs on a playlist and annotate them according to the user’s emotional condition at the moment. To automate this process, many algorithms have been proposed. However, the current algorithms are inefficient, use more hardware, which raises the system’s overall cost, and are less accurate. This paper aims at recognition of human facial expressions through a model based on 68 landmarks approach of the training dataset, further selecting the appropriate playlist for the user. The Emotion Based Music Recommendation System has the potential to enhance the music listening experience by providing users with music that not only matches their preferences but also their emotional state, thereby increasinguser engagement and satisfaction." @default.
- W4386585228 created "2023-09-11" @default.
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- W4386585228 date "2023-06-01" @default.
- W4386585228 modified "2023-09-27" @default.
- W4386585228 title "MOOD PLAYER: EMOTION BASED MUSICRECOMMENDATION SYSTEM" @default.
- W4386585228 doi "https://doi.org/10.33564/ijeast.2023.v08i02.006" @default.
- W4386585228 hasPublicationYear "2023" @default.
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