  Sociometric badges are an emerging technology for study how teams interact in
physical places. Audio data recorded by sociometric badges is often downsampled
to not record discussions of the sociometric badges holders. To gain more
information about interactions inside teams with sociometric badges a Voice
Activity Detector (VAD) is deployed to measure verbal activity of the
interaction. Detecting voice activity from downsampled audio data is
challenging because down-sampling destroys information from the data. We
developed a VAD using deep learning techniques that achieves only moderate
accuracy in a low noise meeting setting and in across variable noise levels
despite excellent validation performance. Experiences and lessons learned while
developing the VAD are discussed.
