Name
Abstract | ||
|---|---|---|
Although abnormal events in an audio stream are by their nature hard to define, a continuous monitoring of audio surveillance data can detect crucial information in, e.g., train engines that might require critical maintenance. Our method detects abnormal events without being trained on a certain situation, by building a model of the expected sound environment given a continuously adapting history of past audio material within a limited time interval. We evaluate the precision of this method on recordings from train rides. |
Year | Venue | Field |
|---|---|---|
2012 | ITG Conference on Speech Communication | Computer science,Speech recognition |
DocType | ISBN | Citations |
Conference | 978-3-8007-3455-9 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rolf Bardeli | 1 | 69 | 8.09 |
| Daniel Stein | 2 | 1 | 0.36 |