The gain in popularity of massive open online courses and other online educational lectures prompts the
investigation of methods for automatically recording such lectures. While most previous systems in this
area have utilized computer vision techniques for tracking, we take an approach utilizing microphone arrays
for both recording audio and tracking lecturers. Different source localization and source tracking methods
are tested, including cross correlation and beamforming methods combined with various state space model
approaches. We investigate how certain constraints granted by a lecture setting may be used to influence
our tracking models, and evaluate the relative strengths and weaknesses of several possible techniques. In
addition, we explore characterizations of the lecture space that allow for the microphone array to work along
with a separate camera to properly record the lecturer's movement. By using the audio to track lecturers
we add
flexibility to the system, but also introduce difficulties in consolidating information between the
microphone array and the camera. Possible methods for communication between the two are addressed, and
we again find that constraints imposed by the lecture setting may be used to resolve such problems.Ope
