  In this paper we study the problem of acoustic scene classification, i.e.,
categorization of audio sequences into mutually exclusive classes based on
their spectral content. We describe the methods and results discovered during a
competition organized in the context of a graduate machine learning course;
both by the students and external participants. We identify the most suitable
methods and study the impact of each by performing an ablation study of the
mixture of approaches. We also compare the results with a neural network
baseline, and show the improvement over that. Finally, we discuss the impact of
using a competition as a part of a university course, and justify its
importance in the curriculum based on student feedback.
