Name
Abstract | ||
|---|---|---|
This paper addresses the problem of automatic detection and recognition of impulsive sounds, such as glass breaks, human screams, gunshots, explosions or door slams. A complete detection and recognition system is described and evaluated on a sound database containing more than 800 signals distributed among six different classes. Emphasis is set on robust techniques, allowing the use of this system in a noisy environment. The detection algorithm, based on a median filter, features a highly ro- bust performance even under important background noise conditions. In the recognition stage, two statistical classifiers are compared, using Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), respec- tively. It can be shown that a rather good recognition rate (98% at 70dB and above 80% for 0dB signal-to-noise ratios) can be reached, even under severe gaussian white noise degradations. |
Year | Venue | Keywords |
|---|---|---|
2000 | Tampere, Finland | Background noise,Gaussian Mixtures,Hidden Markov Models,Impulsive sound detection,Multimodels,Robustness,Sound recognition,Télésurveillance,Tele-assistive technologies |
Field | DocType | ISBN |
Median filter,Background noise,Noise measurement,Sound detection,Pattern recognition,Computer science,Speech recognition,White noise,Robustness (computer science),Artificial intelligence,Hidden Markov model,Mixture model | Conference | 978-952-1504-43-3 |
Citations | PageRank | References |
27 | 3.41 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alain Dufaux | 1 | 34 | 5.61 |
| L. Besacier | 2 | 101 | 12.46 |
| Michael Ansorge | 3 | 49 | 5.68 |
| F. Pellandini | 4 | 46 | 6.05 |