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Computational reproducibility is a growing problem that has been extensivelystudied among computational researchers and within the signal processing andmachine learning research community. However, with the changing landscape ofsignal processing and machine learning research come new obstacles and unseenchallenges in creating reproducible experiments. Due to these new challengesmost computational experiments have become difficult, if not impossible, to bereproduced by an independent researcher. In 2016 a survey conducted by thejournal Nature found that 50% of researchers were unable to reproduce their ownexperiments. While the issue of computational reproducibility has beendiscussed in the literature and specifically within the signal processingcommunity, it is still unclear to most researchers what are the best practicesto ensure reproducibility without impinging on their primary responsibility ofconducting research. We feel that although researchers understand theimportance of making experiments reproducible, the lack of a clear set ofstandards and tools makes it difficult to incorporate good reproducibilitypractices in most labs. It is in this regard that we aim to present signalprocessing researchers with a set of practical tools and strategies that canhelp mitigate many of the obstacles to producing reproducible computationalexperiments.

A Guide to Computational Reproducibility in Signal Processing and Machine Learning