Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal.

Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail party effect . For decades, researchers have focused on approaching the listening ability of humans. One critical issue is handling interfering speakers because the target and nontarget speech signals share similar characteristics, complicating their discrimination. 

A computational experiment is deemed reproducible if the same data and methods are available to replicate quantitative results by any independent researcher, anywhere and at any time, granted that they have the required computing power. Such computational reproducibility is a growing challenge that has been extensively studied among computational researchers as well as within the signal processing and machine learning research community.

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM-imaging tasks. Consequently, generalizability becomes a major concern.

As I am writing this article, I am wrapping up a trip as IEEE Signal Processing Society (SPS) president to Doha, Qatar (9–11 January), to speak at the 2022 IEEE Spoken Language Technology (SLT) Workshop, and India (12–16 January), for technical talks and meetings with local signal processing researchers and SPS local Chapter chairs.