Name
Abstract | ||
|---|---|---|
Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3331184.3331274 | Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | Field | DocType |
cross-modal retrieval, radio story, robust retrieval, sound effect | Heuristic,Information retrieval,Computer science,Inference,Crowdsourcing,Precision and recall,Ambiguity,False positive paradox | Conference |
ISBN | Citations | PageRank |
978-1-4503-6172-9 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songwei Ge | 1 | 13 | 2.57 |
| Curtis Xuan | 2 | 0 | 0.34 |
| Ruihua Song | 3 | 1138 | 59.33 |
| Chao Zou | 4 | 0 | 0.68 |
| Wei Liu | 5 | 0 | 0.34 |
| Jin Zhou | 6 | 0 | 0.34 |