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An Emotion Based Music Recommender System Using Deep Learning

Adebiyi, Sodiq (2020) An Emotion Based Music Recommender System Using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Emotions are affective states which show an individual’s response to mental stimulus. In this paper, we propose the use of Deep Learning and music therapy to influence negative emotions associated with dementia episodes – (Anger, Fear, Sadness and Confusion) and therefore help the patient achieve neutral or positive emotion states – (Joy and Trust). Targeted emotions were identified with the help of a Convoluted Neural Network (CNN) model built on curated datasets containing annotated emotion-labelled audio clips. These audio clips were converted to Mel Frequency Spectrograms (MFS) for classification according to the emotions they represent. We then selected music according to this emotional state using content-based filtering. We achieved average classification results of accuracy: 95.25%, recall: 72.44%, precision: 87.42% and F1 score: 0.7922.
Keywords: Deep Learning, Recommender Systems, Music Therapy, Dementia, Convoluted Neural Network

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 18 Jan 2021 15:44
Last Modified: 18 Jan 2021 15:44

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