Emomusic: An emotion based music player
| dc.contributor.author | Shaikh, Abdus Salam | |
| dc.contributor.author | Rawal, Hasib Ibrahim (16CO45) | |
| dc.contributor.author | Mohd Zeeshan, Mohd Abbas (16CO41) | |
| dc.contributor.author | Khan, Zeeshan Kadeer (16CO33) | |
| dc.date.accessioned | 2021-11-03T07:09:01Z | |
| dc.date.available | 2021-11-03T07:09:01Z | |
| dc.date.issued | 2020-05 | |
| dc.description.abstract | When we talk about the human emotion the human face act as a very important in terms of finding an individual’s mood or emotion. There are various emotions such as happy, sad, angry, etc which can be identified with help of facial expressions. till now if the user wants to make the playlist they have to go through the list of the music then select the songs based on their emotions but it takes consumes more time and it becomes a very tedious and upheld task for the user. Previously many algorithms have been proposed for generating the songs automatically. but the conventional algorithms which are in use are required various external hardware or sensors like electroencephalogram for capturing and identifying the human emotion via human brain it makes the complete process very slow and less accurate. existing systems are not user-friendly they have the complex architecture however This proposed system based on extracted facial expression is user-friendly any user can use it anywhere any time. also proposed system eliminate the task of manually creating the playlists of songs based on the emotions it automatically generates the different playlist It saves much more time and efforts of users who are music lover. Thus the proposed system (Emo-music) aims to minimize the computational time as compared to existing algorithms for getting the results it also reduces the overall cost of the designed system, thereby given features will automatically increase the overall accuracy of the proposed system. The proposed system (Emo-music) tested on both utilize-dependent and utilize-independent datasets. Visages are captured utilizing an inbuilt camera. The precision or Accuracy of the emotion detection algorithm utilized in the system is around 80-95%. Thus, it yields better precision compared to the algorithms utilized in the literature survey. Keywords: Android,Human Face, Emotional Features, PlayList ,User Independent Dataset,User Dependent Dataset, Emotion detection, Inbuilt Camera,Emotion Recognition , Face Recognition,Songsextraction,web scrapping,youtube-dl. | en_US |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3612 | |
| dc.language.iso | en | en_US |
| dc.publisher | AIKTC | en_US |
| dc.subject | Project Report - CO | en_US |
| dc.title | Emomusic: An emotion based music player | en_US |
| dc.type | Other | en_US |