从可察觉的差异中学习的可区分的感知音频度量
A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences
摘要：
许多音频处理任务需要感知评估。获得人类判断的“黄金标准”是耗时的，昂贵的，并且不能用作优化标准。另一方面，自动化指标计算效率高，但通常与人类判断相关性差，特别是对于人类检测阈值处的音频差异。在这项工作中，我们通过将深度神经网络拟合到一个新的大型众包人类判断数据集来构建一个度量。受试者被要求回答一个简单、客观的问题：两个录音是否相同？这些对是在各种扰动下通过算法生成的，包括噪声、混响和压缩伪影;探测扰动空间的目的是有效地识别受试者的刚可察觉差异（JND）水平。我们表明，由此产生的学习度量是很好的校准与人类的判断，优于基线方法。由于它是一个深度网络，因此该度量是可微的，使其适合作为其他任务的损失函数。因此，简单地替换现有的损失（例如，深度特征损失）与我们的度量在去噪网络中产生显著的改进，如通过主观成对比较所测量的。
英文摘要：
Many audio processing tasks require perceptual assessment. The ``gold standard`` of obtaining human judgments is time-consuming, expensive, and cannot be used as an optimization criterion. On the other hand, automated metrics are efficient to compute but often correlate poorly with human judgment, particularly for audio differences at the threshold of human detection. In this work, we construct a metric by fitting a deep neural network to a new large dataset of crowdsourced human judgments. Subjects are prompted to answer a straightforward, objective question: are two recordings identical or not? These pairs are algorithmically generated under a variety of perturbations, including noise, reverb, and compression artifacts; the perturbation space is probed with the goal of efficiently identifying the just-noticeable difference (JND) level of the subject. We show that the resulting learned metric is well-calibrated with human judgments, outperforming baseline methods. Since it is a deep network, the metric is differentiable, making it suitable as a loss function for other tasks. Thus, simply replacing an existing loss (e.g., deep feature loss) with our metric yields significant improvement in a denoising network, as measured by subjective pairwise comparison.
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。
