DocumentCode
13731
Title
Acoustic Scene Classification: Classifying environments from the sounds they produce
Author
Barchiesi, Daniele ; Giannoulis, Dimitrios ; Stowell, Dan ; Plumbley, Mark D.
Author_Institution
Sch. of Electron. Eng. & Comput. Sci., Queen Mary, Univ. of London, London, UK
Volume
32
Issue
3
fYear
2015
fDate
May-15
Firstpage
16
Lastpage
34
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.
Keywords
Gaussian processes; acoustic signal processing; maximum likelihood estimation; mixture models; signal classification; ASC techniques; acoustic scene classification; environmen classification; statistical significance test; Acoustics; Classification algorithms; Feature extraction; Frequency measurement; Hidden Markov models; Image analysis; Signal processing algorithms;
fLanguage
English
Journal_Title
Signal Processing Magazine, IEEE
Publisher
ieee
ISSN
1053-5888
Type
jour
DOI
10.1109/MSP.2014.2326181
Filename
7078982
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