40 citations · 87 across the 18 of their papers we have counts for
14 papers · 1 filter
Sampling Bias Compensation for Robust Evaluation of Audio Classification Systems with Partially Labeled Evaluation Datasets
Javier Naranjo-Alcazar, Annamaria Mesaros, Tuomas Virtanen +1
The performance of acoustic machine learning systems is commonly evaluated using fully annotated test sets. In real-world deployments, however, exhaustively labeling large volumes…
Soroll-IA: A Weakly Labeled Audio Dataset for Real-World Industrial Port Monitoring
Javier Naranjo-Alcazar, Jordi Grau-Haro, Ruben Ribes-Serrano +2
Soroll-IA is a weakly labeled environmental audio dataset recorded in a real-world industrial port environment in Valencia (Spain) using two fixed sensing nodes. The dataset compri…
Comprehensive Evaluation of CNN-Based Audio Tagging Models on Resource-Constrained Devices
Jordi Grau-Haro, Ruben Ribes-Serrano, Javier Naranjo-Alcazar +2
Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in audio tagging tasks. However, deploying these models on resource-constrained devices like the Rasp…
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…
A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning
Javier Naranjo-Alcazar, Jordi Grau-Haro, Ruben Ribes-Serrano +1
Machine Listening focuses on developing technologies to extract relevant information from audio signals. A critical aspect of these projects is the acquisition and labeling of cont…
Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program
Javier Naranjo-Alcazar, Jordi Grau-Haro, David Almenar +1
The Sterile Insect Technique (SIT) is a biological pest control technique based on the release into the environment of sterile males of the insect species whose population is to be…
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations
Josep Zaragoza-Paredes, Javier Naranjo-Alcazar, Valery Naranjo +1
Acoustic scene classification is an automatic listening problem that aims to assign an audio recording to a pre-defined scene based on its audio data. Over the years (and in past e…
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…
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…
Sound Event Localization and Detection using Squeeze-Excitation Residual CNNs
Javier Naranjo-Alcazar, Sergi Perez-Castanos, Jose Ferrandis +2
Sound Event Localization and Detection (SELD) is a problem related to the field of machine listening whose objective is to recognize individual sound events, detect their temporal…
Acoustic Scene Classification with Squeeze-Excitation Residual Networks
Javier Naranjo-Alcazar, Sergi Perez-Castanos, Pedro Zuccarello +1
Acoustic scene classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene…
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
Javier Naranjo-Alcazar, Sergi Perez-Castanos, Pedro Zuccarrello +4
The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show ver…
On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification
Javier Naranjo-Alcazar, Sergi Perez-Castanos, Irene Martin-Morato +2
Residual learning is a recently proposed learning framework to facilitate the training of very deep neural networks. Residual blocks or units are made of a set of stacked layers, w…
CNN depth analysis with different channel inputs for Acoustic Scene Classification
Sergi Perez-Castanos, Javier Naranjo-Alcazar, Pedro Zuccarello +2
Acoustic scene classification (ASC) has been approached in the last years using deep learning techniques such as convolutional neural networks or recurrent neural networks. Many st…