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.
Abstract (translated)
在本文中，我们研究声学场景分类的问题，即，基于音频内容将音频序列分类为互斥类。我们描述了在研究生机器学习课程中组织的比赛中发现的方法和结果;由学生和外部参与者共同完成。我们通过对方法混合进行消融研究来确定最合适的方法并研究每种方法的影响。我们还将结果与神经网络基线进行比较，并显示出相应的改进。最后，我们讨论了将竞赛作为大学课程的一部分的影响，并根据学生的反馈证明其在课程中的重要性。
URL
https://arxiv.org/abs/1808.02357
