A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

Sérgio D Correia,Marko Beko,Slavisa Tomic

doi:10.3390/jsan10020029

Abstract

The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual heavy training process where a low mean squared error is obtained. The networks are implemented, simulated, and compared with conventional algorithms, namely, deterministic and metaheuristic methods, and our results indicate improved performance when noise is added to the measurements. Therefore, the current developed scheme opens up a new horizon for energy-based acoustic localization, a field where machine learning algorithms have not been applied in the past.

Highlights

Mario Antunes and Luis MiguelThe localization of an acoustic source in Wireless Sensors Networks has been commonly employed in several real-life problems
The proposed approach for solving the energy-based acoustic source localization problem relies on a DFNN (Figure 2), where the inputs consist of the measures taken from microphones in the sensors’ network
A new method based on DFNNs for solving the energy-based acoustic localization problem is proposed in the present work

Summary

Introduction

The localization of an acoustic source in Wireless Sensors Networks has been commonly employed in several real-life problems. Examples of its application can be found for energy control of buildings [1], ambient assisted living [2], underwater acoustic networks [3], wildlife monitoring [4], smart surveillance [5], shooter detection [6], or as a complementary source of information to other locating platforms [7,8]. The solution to the problem consists of obtaining measurements that represent the distances from an acoustic source to sensors that acquire the measurement. Contrary to range-free methods, physical measurements such as time-of-arrival [9], time-difference-of-arrival [10], or direction-ofarrival [11] have shown promising results for acquiring distance measures; they rely either on high-precision hardware for timing purposes or on microphone sensor arrays for angle perception. The localization approach considers averaging the energy of the received acoustic signal data samples, standing out for lower bandwidth since it is sampled at a much lower rate [15]

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