论文标题
部分可观测时空混沌系统的无模型预测
Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs
论文作者
论文摘要
神经音频超分辨率模型通常在低分辨率和高分辨率音频信号对上进行训练。尽管这些方法获得了高度准确的超分辨率，如果输入数据的声学特征与培训数据相似，但仍然存在挑战：模型仍遭受质量下域数据的质量降解，并且需要配对数据进行培训。为了解决这些问题，我们提出了Dual-Cyclegan，这是一种高质量的音频超分辨率方法，可以基于两个连接的循环一致的生成对抗网络（Cyclegan）利用未配对的数据。我们的方法将超分辨率方法分解为域的适应和重新采样过程，以处理未配对的低分辨率和高分辨率信号中的声学不匹配。然后在CycleGAN框架内共同优化这两个过程。实验结果证明，当不可用时，提出的方法显着优于常规方法。可从https://chomeyama.github.io/dualcyclegan-demo/获得代码和音频样本。
Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs. Although these methods achieve highly accurate super-resolution if the acoustic characteristics of the input data are similar to those of the training data, challenges remain: the models suffer from quality degradation for out-of-domain data, and paired data are required for training. To address these problems, we propose Dual-CycleGAN, a high-quality audio super-resolution method that can utilize unpaired data based on two connected cycle consistent generative adversarial networks (CycleGAN). Our method decomposes the super-resolution method into domain adaptation and resampling processes to handle acoustic mismatch in the unpaired low- and high-resolution signals. The two processes are then jointly optimized within the CycleGAN framework. Experimental results verify that the proposed method significantly outperforms conventional methods when paired data are not available. Code and audio samples are available from https://chomeyama.github.io/DualCycleGAN-Demo/.
