Acoustic Scene Classification: A Competition Review

TL;DR

This paper reviews methods and results from a competition on acoustic scene classification, highlighting effective approaches, ablation studies, and the educational value of using competitions in university courses.

Contribution

It provides a comprehensive analysis of various methods used in a competition for acoustic scene classification and demonstrates the educational benefits of integrating such competitions into coursework.

Findings

Identification of most effective classification methods

Demonstrated improvement over neural network baseline

Insights into the impact of competition-based learning

Abstract

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.