Reproducible research in signal processing

By Patrick Vandewalle, Jelena Kovacevic, Martin Vetterli. IEEE Signal Processing Magazine, 2009

301 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/1b9adeedd1435fdfe6a3bceb4ec9241c5deaa401

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

Open-Source Practices for Music Signal Processing Research: Recommendations for Transparent, Sustainable, and Reproducible Audio Research (2019, methodology): “We adopt the position previously voiced by numerous authors [6] that description of research systems is no longer sufficient, which follows from the position that scholarly publication serves”
Neuron tracing in perspective (2010, background): “Articles in scientific journals often do not serve this goal, but are merely advertisements of new findings (100), still requiring many days (if not weeks or months) of duplicate work for others to be able to use them productively.”
The Vienna LTE simulators - Enabling reproducibility in wireless communications research (2011, background): “Although reproducibility has a long tradition in most nature sciences and theoretical sciences, such as mathematics, it is only recently that reproducible research has become more and more important in the field of signal processing [1,2].”
Acoustic Scene Classification: Classifying environments from the sounds they produce (2014, background): “initiative is in line with a wider trend in the signal processing community aimed at promoting reproducible research [50].”
Automatic music transcription: challenges and future directions (2013, background): “[122] cite the benefits to the scientific community when research is performed with reproducibility in mind, and well documented code and data are made publicly available: it facilitates building upon others’ work, and allows researchers to spend more time on novel research rather than reimplementin”
On the Reuse of Scientific Data (2017, unknown): “Reproducibility research is more common in computational sciences, where software pipelines can be recorded (Stodden 2010; Vandewalle et al. 2009).”
We get the algorithms of our ground truths: Designing referential databases in digital image processing (2017, background): “Ground truths created by computer science laboratories are made available online in the name of reproducible research (Vandewalle et al., 2009).”