论文标题
使用生成对抗网络的阶段感知音乐超分辨率
Phase-aware music super-resolution using generative adversarial networks
论文作者
论文摘要
音频超分辨率是从低分辨率信号中恢复缺失的高分辨率特征的一项艰巨任务。为了解决这个问题，通过训练低频和高频组件之间的映射，已使用生成对抗网络（GAN）来实现有希望的结果。但是，对于传统方法中的波形重建，相位信息并未得到充分考虑。在本文中，我们解决了音乐超分辨率的问题，并对阶段对此任务的重要性进行了彻底的调查。我们使用GAN预测高频组件的幅度。可以使用基于GAN的波形合成系统或改进的Griffin-LIM算法提取相应的相信息。实验结果表明，相位信息在改善重建音乐质量中起着重要作用。此外，我们提出的方法在客观评估方面大大优于其他最先进的方法。
Audio super-resolution is a challenging task of recovering the missing high-resolution features from a low-resolution signal. To address this, generative adversarial networks (GAN) have been used to achieve promising results by training the mappings between magnitudes of the low and high-frequency components. However, phase information is not well-considered for waveform reconstruction in conventional methods. In this paper, we tackle the problem of music super-resolution and conduct a thorough investigation on the importance of phase for this task. We use GAN to predict the magnitudes of the high-frequency components. The corresponding phase information can be extracted using either a GAN-based waveform synthesis system or a modified Griffin-Lim algorithm. Experimental results show that phase information plays an important role in the improvement of the reconstructed music quality. Moreover, our proposed method significantly outperforms other state-of-the-art methods in terms of objective evaluations.
