Benchmarking multi-component signal processing methods in the time-frequency plane
CoRR(2024)
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.
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