activity
20192025
most citedAcoustic Scene Classification with Squeeze-Excitation Residual Networks

40 citations · 107 across the 14 of their papers we have counts for

collaborators

15 papers

cs.SD2025

Spike Encoding for Environmental Sound: A Comparative Benchmark

Andres Larroza, Javier Naranjo-Alcazar, Vicent Ortiz +2

Spiking Neural Networks (SNNs) offer energy efficient processing suitable for edge applications, but conventional sensor data must first be converted into spike trains for neuromor…

eess.AS2023★ 12 cited

Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations

Maximo Cobos, Mirco Pezzoli, Fabio Antonacci +1

Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays. A very important…

cs.SD2021★ 1 cited

Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint

Javier Naranjo-Alcazar, Sergi Perez-Castanos, Maximo Cobos +2

Acoustic scene classification (ASC) is one of the most popular problems in the field of machine listening. The objective of this problem is to classify an audio clip into one of th…

cs.SD2021

TASK3 DCASE2021 Challenge: Sound event localization and detection using squeeze-excitation residual CNNs

Javier Naranjo-Alcazar, Sergi Perez-Castanos, Pedro Zuccarello +2

Sound event localisation and detection (SELD) is a problem in the field of automatic listening that aims at the temporal detection and localisation (direction of arrival estimation…

cs.MM2021★ 3 cited

Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification

Javier Naranjo-Alcazar, Sergi Perez-Castanos, Aaron Lopez-Garcia +3

The use of multiple and semantically correlated sources can provide complementary information to each other that may not be evident when working with individual modalities on their…

cs.IT2021★ 21 cited

Fast Channel Estimation in the Transformed Spatial Domain for Analog Millimeter Wave Systems

Sandra Roger, Maximo Cobos, Carmen Botella-Mascarell +1

Fast channel estimation in millimeter-wave (mmWave) systems is a fundamental enabler of high-gain beamforming, which boosts coverage and capacity. The channel estimation stage typi…