자기 지도 학습 특징을 활용한 음성 신호의 논 블라인드 대역폭 확장

Bandwidth extension (BWE) is a technique that estimates high-frequency components from provided low-frequency components. Most existing deep neural network-based BWE methods rely solely on information from the band-limited audio signal. Drawing inspiration from spectral band replication (SBR), we propose a novel BWE framework that employs self-supervised pre-trained features as conditional inputs in the BWE process. Our experiments demonstrate that the proposed approach outperforms the baseline SEANet architecture in terms of log-spectral distance (LSD) and PESQ. Additionally, we explore the methodologies for extracting and quantizing self-supervised learning (SSL) features to effectively utilize them as conditional inputs.