Acoustic Scene Classification: A Competition Review
원문 초록
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.
함께 살펴볼 논문
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Spatial Information Considered Network for Scene Classification
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge
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Generalized Scene Classification from Small-Scale Datasets with Multi-Task Learning
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
토론
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