Skip to main content

Research Repository

Advanced Search

A recurrent neural network for sound-source motion tracking and prediction

Murray, J. C.; Erwin, H.; Wermter, S.

Authors

J. C. Murray

H. Erwin

S. Wermter



Abstract

Recurrent neural networks (RNN) have been used in many applications for both pattern detection and prediction. This paper shows the use of RNN's as a speed classifier and predictor for a robotic sound source tracking system. The system requires extensive training to classify all possible speeds to enable dynamic tracking of the most prominent sound within the environment.

Citation

Murray, J. C., Erwin, H., & Wermter, S. (2005). A recurrent neural network for sound-source motion tracking and prediction. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 (2232-2236). https://doi.org/10.1109/IJCNN.2005.1556248

Conference Name Proceedings of the International Joint Conference on Neural Networks
Conference Location Montreal, Que., Canada
Start Date Jul 31, 2004
End Date Aug 4, 2005
Online Publication Date Dec 27, 2005
Publication Date Dec 1, 2005
Deposit Date Oct 26, 2018
Publicly Available Date Mar 28, 2024
Volume 4
Pages 2232-2236
Series ISSN 2161-4393
Book Title Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
ISBN 0780390482
DOI https://doi.org/10.1109/IJCNN.2005.1556248
Keywords Recurrent neural networks; Tracking; Robot sensing systems; Signal to noise ratio; Azimuth; Microphones; Hybrid intelligent systems; Manufacturing; Human robot interaction; Navigation
Public URL https://hull-repository.worktribe.com/output/799639
Publisher URL https://ieeexplore.ieee.org/document/1556248