Name
Abstract | ||
|---|---|---|
Tagging of sound events is essential in many research areas. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. Building an automatic recognition system using machine learning techniques is often not feasible because it requires a large number of human-labeled training examples and fine tuning the model for a specific application. Fully automated labeling is also not reliable enough for all uses. We present I-SED, an interactive sound detection interface using a human-in-the-loop approach that lets a user reduce the time required to label audio that is tediously long (e.g. 20 hours) to do manually and has too few prior labeled examples (e.g. one) to train a state-of-the-art machine audio labeling system. We performed a human-subject study to validate its effectiveness and the results showed that our tool helped participants label all target sound events within a recording twice as fast as labeling them manually. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3025171.3025231 | IUI |
Keywords | Field | DocType |
interactive machine learning, sound event detection, human-in-the-loop system | Sound detection,Recognition system,Computer science,Fine-tuning,Speech recognition,Sound event detection,Detector | Conference |
Citations | PageRank | References |
6 | 0.51 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bongjun Kim | 1 | 15 | 5.22 |
| Bryan Pardo | 2 | 830 | 63.92 |