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Acoustic Scene Classification: Classifying environments from the sounds they produce

Barchiesi, D, Giannoulis, D, Stowell, D and Plumbley, MD (2015) Acoustic Scene Classification: Classifying environments from the sounds they produce IEEE Signal Processing Magazine, 32 (3). pp. 16-34.

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Abstract

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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Barchiesi, DUNSPECIFIEDUNSPECIFIED
Giannoulis, DUNSPECIFIEDUNSPECIFIED
Stowell, DUNSPECIFIEDUNSPECIFIED
Plumbley, MDUNSPECIFIEDUNSPECIFIED
Date : May 2015
Identification Number : 10.1109/MSP.2014.2326181
Uncontrolled Keywords : Acoustics, Classification algorithms, Feature extraction, Frequency measurement, Hidden Markov models, Signal processing algorithms, Transforms
Related URLs :
Additional Information : (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 13 May 2015 15:07
Last Modified : 03 Aug 2016 18:10
URI: http://epubs.surrey.ac.uk/id/eprint/807420

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