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

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

Compared to large speech foundation models, small student models exhibit degraded noise
robustness. The student's robustness can be improved by introducing noise at the inputs …

Self-supervised learning (SSL) using masked prediction has made great strides in general-
purpose audio representation. This study proposes Masked Modeling Duo (M2D), an …

With advances in deep learning methodologies, Automatic Speech Recognition (ASR)
systems have seen impressive results. However, ASR in Low-Resource Environments …

We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification,
which brings the concept of multiscale feature hierarchies to the Audio Spectrogram …

With the development of the Internet and new media, multimedia and audio learning
resources have been widely used in teaching and learning. However, their classification and …

We present a new Self-Supervised Learning (SSL) approach to pre-train encoders on
unlabeled audio data that reduces the need for large amounts of labeled data for audio and …

Continued pre-training (CP) offers multiple advantages, like target domain adaptation and
the potential to exploit the continuous stream of unlabeled data available online. However …

In this paper, we propose the WaveBYOL model, which can learn general-purpose audio
representations directly from raw waveforms based on the bootstrap your own latent (BYOL) …