//Personalised music selections effecting emotion and mood

Personalised music selections effecting emotion and mood


Research study which involved development of client and server applications to curate personalised playlists based on predictive computational models.

I was involved in two main directions of this study:
(1) Personalised music selections for driving scenarios
(2) Personalised music selections for mitigating the symptoms of depression

Personalised music selections for driving scenarios

Development of applications to facilitate driving experiments to investigate the impact of listening to music while driving and driving behaviour.

Personalised music selections for mitigating the symptoms of depression

Since low mood is a critical symptom of depressive disorders, music emerges as a potential therapeutic tool due to its effectiveness for the induction of emotional and mood states. Music is commonly used by people in everyday life to regulate affective states, and it has demonstrated effects in mood regulation both in clinical and non-clinical populations

Music player application was developed which could help to influence mood based on computational model to control personalised playlists.

This application was integrated with the online music service Spotify such that a users music selection could be derived from favourite tracks or genre of music.

Software

Polyhymnia Driving Application
Gitrepo: music-driving

Polyhymnia Android Application
Gitrepo: polyhymnia-player-android

Polyhymnia Server Desktop Application
Gitrepo: polyhymnia-local

Polyhymnia Spotify Application
Gitrepo: polyhymnia-spotify

Further Reading

This is a list of software and code snippets used as part of the project that might be of interest.

  • http://ayeshalshukri.co.uk/technical-guides/how-to-install-tensor-flow-on-ubuntu/
  • http://ayeshalshukri.co.uk/technical-guides/how-to-install-tflearn/