基于等变性的自监督学习用于从削波测量中恢复音频信号
Equivariance-based self-supervised learning for audio signal recovery from clipped measurements
AI总结：
针对从削波测量中恢复音频信号的非线性逆问题，提出一种基于等变性的自监督学习方法，仅需削波数据训练即可取得媲美全监督学习的性能。
摘要：
在众多逆问题中，最先进的求解策略涉及从真实数据和相关的测量数据集中训练神经网络，然而，这些数据可能收集成本高昂或根本无法收集。最近，自监督学习技术应运而生，其主要优势是不再需要真实数据。目前关于自监督学习的大多数理论和实验结果都集中在线性逆问题上。本研究旨在针对从削波测量中恢复音频信号这一非线性逆问题，探讨其自监督学习方法。我们提出并研究了一种基于等变性的自监督损失函数。性能评估在模拟的削波测量上进行，其具有受控且多样的削波水平，并进一步在标准真实音乐信号上进行了报告。我们表明，所提出的基于等变性的自监督去削波策略的性能与全监督学习相比更具优势，同时训练时仅需削波测量数据。
英文摘要：
In numerous inverse problems, state-of-the-art solving strategies involve training neural networks from ground truth and associated measurement datasets that, however, may be expensive or impossible to collect. Recently, self-supervised learning techniques have emerged, with the major advantage of no longer requiring ground truth data. Most theoretical and experimental results on self-supervised learning focus on linear inverse problems. The present work aims to study self-supervised learning for the non-linear inverse problem of recovering audio signals from clipped measurements. An equivariance-based selfsupervised loss is proposed and studied. Performance is assessed on simulated clipped measurements with controlled and varied levels of clipping, and further reported on standard real music signals. We show that the performance of the proposed equivariance-based self-supervised declipping strategy compares favorably to fully supervised learning while only requiring clipped measurements alone for training.
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。
