Paper Info
Title
Near field acoustic localization under unfavorable conditions using feedforward neural network for processing time difference of arrival.
Abstract
Providing guidelines for practical implementation of neural networks in near-field sound source localization.Obtained optimal sensors setups.Obtaining optimal network configuration.Obtaining optimal training parameters.Proving effectiveness of feedforward neural network in solving hyperbolic positioning problem under the uncertainties. Using time difference of arrival (TDOA) is one of the two approaches that utilize time delay for acoustic source localization. Combining the obtained TDOAs together with geometrical relationships within acoustic components results in a system of hyperbolic equations. Solving these hyperbolic equations is not a trivial procedure especially in the case of a large number of microphones. The solution is additionally compounded by uncertainties of different backgrounds. The paper investigates the performance of neural networks in modelling a hyperbolic positioning problem using a feedforward neural network as a representative. For experimental purposes, more than 2000 sound files were recorded by 8 spatially disposed microphones, for as many arbitrarily chosen acoustic source positions. The samples were corrupted by high level correlated noise and reverberation. Using cross-correlation, with previous signal pre-processing, TDOAs were evaluated for every pair of microphones. On the basis of the obtained TDOAs and accurate sound source positions, the neural network was trained to perform sound source localization. The performance was examined using a large number of samples in terms of different acoustic sensors setups, network configurations and training parameters. The experiment provided useful guidelines for the practical implementation of feedforward neural networks in the near-field acoustic localization. The procedure does not require substantial knowledge of signal processing and that is why it is suitable for a broad range of users.
Year
DOI
Venue
2017
10.1016/j.eswa.2016.11.030
Expert Syst. Appl.
Keywords
Field
DocType
Processing time difference,Acoustic source localization,Time difference of arrival,Feedforward neural network,Artificial intelligence
Signal processing,Feedforward neural network,Reverberation,Computer science,Near and far field,Artificial intelligence,Multilateration,Artificial neural network,Machine learning,Acoustic source localization,Hyperbolic partial differential equation
Journal
Volume
Issue
ISSN
71
C
0957-4174
Citations 
PageRank 
References 
2
0.39
9
Authors
5
Name
Order
Citations
PageRank
Marko Kovandzic120.39
Vlastimir Nikolic2122.03
Abdulathim Al-Noori320.39
Ivan Ciric421.06
Milos Simonovic520.73