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| Content Provider | ACM Digital Library |
|---|---|
| Author | Konan, N'djabli Cedric Ange Arakawa, Yutaka Suwa, Hirohiko Yasumoto, Keiichi |
| Abstract | It is essential to select/assign appropriate background mu- sic (BGM) for each scene/cut when we edit a video or a slideshow of photos. However, it is a laborious task. Aim- ing to realise automatic BGM selection/assignment, we pro- pose a method to automatically assign emotion tag to various BGM. To realise this method, we need a model for classify- ing BGM. To build our model, we use a set of movie scene BGMs that a group of 14 users tagged with five (5) differ- ent sentiments: Love, Surprise, Joy, Sadness, and Fear. Af- ter confirming their agreements, we extracted the features of each audio file of our dataset. Using the machine-learning tool WEKA and the random forest algorithm, we built a model. Through a cross validation process, we evaluated our model and obtained an accuracy of 94% in prediction of the emotion in the BGM, demonstrating the effectiveness of the proposed approach. |
| Starting Page | 47 |
| Ending Page | 47 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781450344166 |
| DOI | 10.1145/2938559.2948770 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Crowdsourcing Sentiment analysis Music Machine learning Bgm Emotion |
| Content Type | Text |
| Resource Type | Article |
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