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.

翻译：时频平面上的信号处理历史悠久，且至今仍是方法创新的领域。例如，自2015年以来，基于频谱图零点的检测与去噪方法被提出，这与长期以来关注频谱图较大值的传统形成对比。然而，与优化、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。本研究贡献了一个基于Python的开源工具箱MCSM-Benchs，用于多分量信号分析方法的基准测试，并通过三个时频基准测试案例展示其应用。首先，我们比较了基于频谱图零点的不同信号检测方法，包括前人提出的检测测试中未被探索的变体。其次，将基于零点的去噪方法与基于频谱图较大值和脊线的经典及新颖方法进行比较。最后，从常被称为“音乐噪声”的后处理伪影角度，比较了这些方法与传统频谱图阈值策略的去噪性能。在微观层面，所得结果为所评估方法提供了新见解，尤其为零点方法的进一步发展指明了研究方向。在宏观层面，本基准测试彰显了采用公共协作通用框架进行基准测试的优势。