activity
20182025
most citedTowards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

112 citations · 133 across the 4 of their papers we have counts for

collaborators

5 papers

physics.geo-ph2025★ 1 cited

Vessel Detection and Localization Using Distributed Acoustic Sensing in Submarine Optical Fiber Cables

Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Daniel Pizarro-Perez +6

Submarine cables play a critical role in global internet connectivity, energy transmission, and communication but remain vulnerable to accidental damage and sabotage. Recent incide…

cs.CV2020

Towards Dense People Detection with Deep Learning and Depth images

David Fuentes-Jimenez, Cristina Losada-Gutierrez, David Casillas-Perez +4

This paper proposes a DNN-based system that detects multiple people from a single depth image. Our neural network processes a depth image and outputs a likelihood map in image coor…

eess.SP2020

Towards Domain Independence in CNN-based Acoustic Localization using Deep Cross Correlations

Juan Manuel Vera-Diaz, Daniel Pizarro, Javier Macias-Guarasa

Time delay estimation is essential in Acoustic Source Localization (ASL) systems. One of the most used techniques for this purpose is the Generalized Cross Correlation (GCC) betwee…

cs.CV2020★ 20 cited

DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera

David Fuentes-Jimenez, Roberto Martin-Lopez, Cristina Losada-Gutierrez +4

In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, i…

cs.SD2018★ 112 cited

Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

Juan Manuel Vera-Diaz, Daniel Pizarro, Javier Macias-Guarasa

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to…