Acoustic Scene Classification: A Competition Review
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.
Keywords
Cite
@article{arxiv.1808.02357,
title = {Acoustic Scene Classification: A Competition Review},
author = {Shayan Gharib and Honain Derrar and Daisuke Niizumi and Tuukka Senttula and Janne Tommola and Toni Heittola and Tuomas Virtanen and Heikki Huttunen},
journal= {arXiv preprint arXiv:1808.02357},
year = {2024}
}
Comments
This work has been accepted in IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2018)