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n2:91418
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bibo:Article n6:ConferenceItemEPrint bibo:AcademicArticle n6:EPrint
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n4:
dcterms:title
A robust framework for acoustic scene classification
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dcterms:date
2019-09-20
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n8:ext-h.phan@kent.ac.uk n8:ext-i.v.mcloughlin@kent.ac.uk n8:ext-r.palani@kent.ac.uk n8:ext-ldp7@kent.ac.uk
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n11:peerReviewed n11:published
bibo:abstract
Acoustic scene classification (ASC) using front-end timefrequency features and back-end neural network classifiers has demonstrated good performance in recent years. However a profusion of systems has arisen to suit different tasks and datasets, utilising different feature and classifier types. This paper aims at a robust framework that can explore and utilise a range of different time-frequency features and neural networks, either singly or merged, to achieve good classification performance. In particular, we exploit three different types of frontend time-frequency feature; log energy Mel filter, Gammatone filter and constant Q transform. At the back-end we evaluate effective a two-stage model that exploits a Convolutional Neural Network for pre-trained feature extraction, followed by Deep Neural Network classifiers as a post-trained feature adaptation model and classifier. We also explore the use of a data augmentation technique for these features that effectively generates a variety of intermediate data, reinforcing model learning abilities, particularly for marginal cases. We assess performance on the DCASE2016 dataset, demonstrating good classification accuracies exceeding 90%, significantly outperforming the DCASE2016 baseline and highly competitive compared to state-of-the-art systems.
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