在时频平面上对多分量信号处理方法进行基准测试
Benchmarking multi-component signal processing methods in the time-frequency plane
AI总结：
本文提出开源Python工具箱MCSM-Benchs，在三个时频基准上系统比较了基于spectrogram零点、大值及脊线的信号检测与去噪方法，为时频信号处理提供了通用基准测试框架。
摘要：
时频平面上的信号处理历史悠久，且仍是一个充满方法论创新的领域。例如，基于 spectrogram 零点的检测与去噪方法自2015年起即被提出，这与长期以来关注 spectrogram 较大值的传统形成了对比。然而，与优化和机器学习等相邻领域不同，时频信号处理缺乏广泛采用的基准测试工具。在本研究中，我们贡献了一个开源的、基于 Python 的工具箱，称为 MCSM-Benchs，用于对多分量信号分析方法进行基准测试，并在三个时频基准上展示了我们的工具箱。首先，我们比较了基于 spectrogram 零点的不同信号检测方法，包括先前提出的检测测试中尚未探索的变体。其次，我们将基于零点的去噪方法与基于 spectrogram 大值和脊线的经典及新颖方法进行了比较。最后，我们比较了这些方法与典型的 spectrogram 阈值策略的去噪性能，评估指标为通常被称为音乐噪声的后处理伪影。在较低层面上，所获得的结果为所评估的方法提供了新见解，特别是进一步发展基于零点方法的研究方向。在较高层面上，我们的基准测试例证了使用公共、协作、通用框架进行基准测试的益处。
英文摘要：
Signal processing in the time-frequency plane has a long history and remains a field of methodological innovation. For instance, detection and denoising based on the zeros of the spectrogram have been proposed since 2015, contrasting with a long history of focusing on larger values of the spectrogram. Yet, unlike neighboring fields like optimization and machine learning, time-frequency signal processing lacks widely-adopted benchmarking tools. In this work, we contribute an open-source, Python-based toolbox termed MCSM-Benchs for benchmarking multi-component signal analysis methods, and we demonstrate our toolbox on three time-frequency benchmarks. First, we compare different methods for signal detection based on the zeros of the spectrogram, including unexplored variations of previously proposed detection tests. Second, we compare zero-based denoising methods to both classical and novel methods based on large values and ridges of the spectrogram. Finally, we compare the denoising performance of these methods against typical spectrogram thresholding strategies, in terms of post-processing artifacts commonly referred to as musical noise. At a low level, the obtained results provide new insight on the assessed approaches, and in particular research directions to further develop zero-based methods. At a higher level, our benchmarks exemplify the benefits of using a public, collaborative, common framework for benchmarking.
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。
