2 papers
cs.SD2026
MiCLA: Mixture-to-Constituent Latent Alignment for Polyphonic Audio Representation Learning
Kohei Yamamoto, Kosuke Okusa
Real-world audio typically contains concurrent sound events, yet standard self-supervised methods treat each clip as an isolated training unit without explicitly modeling mixture--…
cs.SD2025
AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers
Kohei Yamamoto, Kosuke Okusa
Transformer-based audio self-supervised learning (SSL) models commonly use spectrograms, vision-style Transformers, and masked modeling objectives. However, convolutional patchific…