This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship
(EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council

Barchiesi, D

Giannoulis, D

Plumbley, MD

Stowell, D

IEEE Signal Processing Magazine

English

arXiv

Daniele Barchiesi

Dimitrios Giannoulis

Dan Stowell

Mark D. Plumbley

Crossref

Acoustic Scene Classification: Classifying environments from the sounds they produce

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Acoustic Scene Classification

In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods

Acoustic Scene Classification

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