Learning-based methods have become ubiquitous in speaker localization. Existing systems
rely on simulated training sets for the lack of sufficiently large, diverse and annotated real …

Audio classification aims at recognizing audio signals, including speech commands or
sound events. However, current audio classifiers are susceptible to perturbations and …

In this work we present a large set of simulated room impulse responses for a multi-room
apartment. The simulated apartment models a real vacation apartment for which a recorded …

We propose a sound event localization and detection system based on a CNN-Conformer
base network. Our main contribution is to evaluate the use of pre-trained elements in this …

Learning-based methods have become ubiquitous in sound source localization (SSL).
Existing systems rely on simulated training sets for the lack of sufficiently large, diverse and …

Acoustic sensing has been widely exploited for the early detection of harmful situations in
urban environments: in particular, several siren identification algorithms based on deep …

Environment shifts and conflicts present significant challenges for learning-based sound
event localization and detection (SELD) methods. SELD systems, when trained in particular …

This study focuses on the design of a loss function for a deep neural network (DNN)-based
model with two branches, which is used to solve sound event localization and detection …

Sound event localization and detection (SELD) has seen substantial advancements through
learning-based methods. These systems, typically trained from scratch on specific datasets …

In this study, we present a simple multi-channel framework for contrastive learning (MC-
SimCLR) to encode 'what'and 'where'of spatial audios. MC-SimCLR learns joint spectral and …