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eess.AS2025
Towards Blind Data Cleaning: A Case Study in Music Source Separation
Azalea Gui, Woosung Choi, Junghyun Koo +5
The performance of deep learning models for music source separation heavily depends on training data quality. However, datasets are often corrupted by difficult-to-detect artifacts…
eess.AS2022★ 11 cited
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Ryosuke Sawata, Naoki Murata, Yuhta Takida +4
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.…