Preserving training dynamics across batch sizes is an important tool for practical machine
learning as it enables the trade-off between batch size and wall-clock time. This trade-off is …

Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics.
Most prominent work in this area focuses on learning controllers or policies that either …

Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it
learns representations indirectly by reconstructing masked input patches. Several methods …

Self-supervised learning (SSL) has emerged as a popular approach for learning audio
representations. One goal of audio self-supervised pre-training is to transfer knowledge to …

Modern speech enhancement (SE) networks typically implement noise suppression through
time-frequency masking, latent representation masking, or discriminative signal prediction …

We present XKD, a novel self-supervised framework to learn meaningful representations
from unlabelled videos. XKD is trained with two pseudo objectives. First, masked data …

State-of-the-art anomalous sound detection (ASD) systems are often trained by using an
auxiliary classification task to learn an embedding space. Doing so enables the system to …

Audio embeddings are crucial tools in understanding large catalogs of music. Typically
embeddings are evaluated on the basis of the performance they provide in a wide range of …

Self-supervised learning (SSL) in audio holds significant potential across various domains,
particularly in situations where abundant, unlabeled data is readily available at no cost. This …

Limited diversity in standardized benchmarks for evaluating audio representation learning
(ARL) methods may hinder systematic comparison of current methods' capabilities. We …