Name
Abstract | ||
|---|---|---|
In this paper we propose a novel approach to the detection of acoustic irregular signals using Minimum Detection Error (MDE) training. The MDE training is based on the Generalized Probabilistic Descent method, which was originally developed as a general concept for discriminative pattern recognizer design. We demonstrate its fundamental utility by experiments in which several acoustic events are detected in a noisy environment. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICASSP.1998.675484 | PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6 |
Keywords | Field | DocType |
neural nets,learning artificial intelligence,pattern recognition,model driven engineering,signal processing,acoustic noise | Noise,Signal monitoring,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Probabilistic logic,Artificial neural network,Discriminative model | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.43 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideyuki Watanabe | 1 | 37 | 8.46 |
| yuji matsumoto | 2 | 3008 | 300.05 |
| Shigeru Katagiri | 3 | 850 | 114.01 |