Acoustic scene classification: An overview of dcase 2017 challenge entries


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Mesaros, Annamaria; Heittola, Toni; Virtanen, Tuomas

Abstract

We present an overview of the challenge entries for the Acoustic Scene Classification task of DCASE 2017 Challenge. Being the most popular task of the challenge, acoustic scene classification entries provide a wide variety of approaches for comparison, with a wide performance gap from top to bottom. Analysis of the submissions confirms once more the popularity of deep-learning approaches and mel frequency representations. Statistical analysis indicates that the top ranked system performed significantly better than the others, and that combinations of top systems are capable of reaching close to perfect performance on the given data.

Keywords

Acoustic scene classification; Audio classb ification; DCASE challenge

Research areas

Year:
2018
Book title:
16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018
Pages:
411-415
Month:
11
DOI:
10.1109/IWAENC.2018.8521242