5 papers
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…
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…
Threat Modeling for Enhancing Security of IoT Audio Classification Devices under a Secure Protocols Framework
Sergio Benlloch-Lopez, Miquel Viel-Vazquez, Javier Naranjo-Alcazar +2
The rapid proliferation of IoT nodes equipped with microphones and capable of performing on-device audio classification exposes highly sensitive data while operating under tight re…
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…