BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations
arXiv:2606.30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks. We present BEST-RQ-2, an evolution of BEST-RQ that retains frozen randomprojection-based discrete targets while introducing a two-step contextualize-then-predict pretraining scheme. A ViT context encoder processes only the unmasked spectrogram regions, and a lightweight predictor infers targets for the masked regions; the predictor is discarded after pretraining. Replacing the original Conformer encoder with a ViT shifts performance across domains, sl
The continuous evolution of self-supervised learning techniques in AI, driven by increasing computational capabilities and a demand for more efficient data utilization, makes this development timely.
This new method streamlines the pretraining process for audio AI, potentially leading to more advanced and efficient AI models for various audio-related applications.
The adoption of a two-step contextualize-then-predict approach with a Vision Transformer (ViT) allows for more efficient self-supervised learning in audio, potentially reducing computational overhead and improving model performance across domains.
- · AI researchers
- · Audio AI developers
- · Companies utilizing speech recognition
- · AI hardware manufacturers
- · Developers relying on less efficient legacy audio AI architectures
Improved performance and efficiency in audio processing AI models.
Faster development cycles for new audio applications and services.
Enhanced AI capabilities in areas like voice assistants, autonomous systems' sensory input, and sound analysis for surveillance or medical diagnostics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG