论文标题
Aero：光谱域中的音频超级分辨率
AERO: Audio Super Resolution in the Spectral Domain
论文作者
论文摘要
我们提出了Aero，这是一种音频超分辨率模型，该模型在光谱域中处理语音和音乐信号。 AERO基于具有u-net之类的编码器架构，例如跳过连接。我们使用时间域和频域损耗函数优化模型。具体而言，我们以对抗性和特征歧视损失函数的形式考虑了一组重建损失以及感知损失。为了更好地处理相位信息，提出的方法使用两个单独的通道在复杂值频谱图上运行。与先前的工作主要考虑音频超级分辨率的低频和高频串联不同，该提出的方法直接预测了全频率范围。考虑到语音和音乐，我们在广泛的样本率中展示了高性能。考虑到对数 - 光谱距离，粘胶和主观Mushra检验的均超过评估的基线。音频示例和代码可在https://pages.cs.huji.ac.il/adiyoss-lab/aero上找到
We present AERO, a audio super-resolution model that processes speech and music signals in the spectral domain. AERO is based on an encoder-decoder architecture with U-Net like skip connections. We optimize the model using both time and frequency domain loss functions. Specifically, we consider a set of reconstruction losses together with perceptual ones in the form of adversarial and feature discriminator loss functions. To better handle phase information the proposed method operates over the complex-valued spectrogram using two separate channels. Unlike prior work which mainly considers low and high frequency concatenation for audio super-resolution, the proposed method directly predicts the full frequency range. We demonstrate high performance across a wide range of sample rates considering both speech and music. AERO outperforms the evaluated baselines considering Log-Spectral Distance, ViSQOL, and the subjective MUSHRA test. Audio samples and code are available at https://pages.cs.huji.ac.il/adiyoss-lab/aero
