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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Moiseff, A. Palmieri, F. Datum, M. Shah, A. |
| Copyright Year | 1991 |
| Description | Author affiliation: Connecticut Univ., Storrs, CT, USA (Moiseff, A.; Palmieri, F.; Datum, M.; Shah, A.) |
| Abstract | A three-layer neural network is used to solve the problem of extracting relative azimuth and elevation positional information from signals detected by two directional receivers that are spatially separate. This is analogous to the ability of owls to localize the position of sound sources based solely on the properties of the signals reaching the two ears. Although the network implemented does not require any specific knowledge about acoustical parameters or propagation properties, a simple model of the acoustical environment is used to generate simulated data for training the network. The neural network is trained according to the multiple extended Kalman algorithm. The network successfully transforms phase and intensity differences of simulated acoustical signals into relative azimuth and elevation consistent with the simulated model.< |
| Starting Page | 1 |
| Ending Page | 2 |
| File Size | 198801 |
| Page Count | 2 |
| File Format | |
| ISBN | 0780300300 |
| DOI | 10.1109/NEBC.1991.154551 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-04-04 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Artificial neural networks Ear Azimuth Modeling Neural networks Frequency Data mining Eyes Physiology Acoustical engineering |
| Content Type | Text |
| Resource Type | Article |
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