音乐超分辨率中时域卷积神经网络的相位修复
Phase Repair for Time-Domain Convolutional Neural Networks in Music Super-Resolution
AI总结：
针对TD-CNN在音乐超分辨率中产生伪影的问题，首次通过实验证明其由相位失真引起，并提出TD-PR方法利用预训练声码器修复相位，显著提升了多种TD-CNN基线的感知质量。
摘要：
音频超分辨率（SR）是一个重要的课题，因为低分辨率录音在日常生活中无处不在。本文关注音乐SR任务，由于音乐具有宽广的频率响应和动态范围，该任务极具挑战性。许多模型在时域中设计，以联合处理音频信号的幅度和相位。然而，先前的工作表明，使用时域卷积神经网络（TD-CNN）的方法倾向于在其波形输出中产生令人烦恼的伪影，且伪影的原因尚待确定。据我们所知，这项工作是首次通过主观实验证明TD-CNN中的伪影是由相位失真引起的。我们进一步提出了时域相位修复（TD-PR），它使用在宽带数据上预训练的神经声码器来修复TD-CNN波形输出中的相位分量。尽管声码器和TD-CNN是独立训练的，所提出的TD-PR获得了更好的平均意见分，显著提升了TD-CNN基线的感知质量。由于所提出的TD-PR仅修复波形的相位分量，提升的感知质量反过来表明相位失真一直是TD-CNN令人烦恼的伪影的原因。此外，单个预训练的声码器可以直接应用于任意TD-CNN，无需额外适应。因此，我们将TD-PR应用于具有不同架构和参数量的三个TD-CNN。当TD-PR应用于所有三个TD-CNN基线时，观察到了一致的提升。演示页面上提供了音频样本。
英文摘要：
Audio Super-Resolution (SR) is an important topic as low-resolution recordings are ubiquitous in daily life. In this paper, we focus on the music SR task, which is challenging due to the wide frequency response and dynamic range of music. Many models are designed in time domain to jointly process magnitude and phase of audio signals. However, prior works show that approaches using Time-Domain Convolutional Neural Network (TD-CNN) tend to produce annoying artifacts in their waveform outputs, and the cause of the artifacts is yet to be identified. To the best of our knowledge, this work is the first to demonstrate the artifacts in TD-CNNs are caused by the phase distortion via a subjective experiment. We further propose Time-Domain Phase Repair (TD-PR), which uses a neural vocoder pre-trained on the wide-band data to repair the phase components in the waveform outputs of TD-CNNs. Although the vocoder and TD-CNNs are independently trained, the proposed TD-PR obtained better mean opinion score, significantly improving the perceptual quality of TD-CNN baselines. Since the proposed TD-PR only repairs the phase components of the waveforms, the improved perceptual quality in turn indicates that phase distortion has been the cause of the annoying artifacts of TD-CNNs. Moreover, a single pretrained vocoder can be directly applied to arbitrary TD-CNNs without additional adaptation. Therefore, we apply TD-PR to three TD-CNNs that have different architecture and parameter amount. Consistent improvements are observed when TD-PR is applied to all three TD-CNN baselines. Audio samples are available on the demo page.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
