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Deep Neural Network Baseline for DCASE Challenge 2016

Kong, Qiuqiang, Sobieraj, Iwona, Wang, Wenwu and Plumbley, Mark (2016) Deep Neural Network Baseline for DCASE Challenge 2016 In: Detection and Classification of Acoustic Scenes and Events 2016, 2016-09-03, Budapest, Hungary.

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Abstract

The DCASE Challenge 2016 contains tasks for Acoustic Scene Classification (ASC), Acoustic Event Detection (AED), and audio tagging. Since 2006, Deep Neural Networks (DNNs) have been widely applied to computer visions, speech recognition and natural language processing tasks. In this paper, we provide DNN baselines for the DCASE Challenge 2016. In Task 1 we obtained accuracy of 81.0% using Mel + DNN against 77.2% by using Mel Frequency Cepstral Coefficients (MFCCs) + Gaussian Mixture Model (GMM). In Task 2 we obtained F value of 12.6% using Mel + DNN against 37.0% by using Constant Q Transform (CQT) + Nonnegative Matrix Factorization (NMF). In Task 3 we obtained F value of 36.3% using Mel + DNN against 23.7% by using MFCCs + GMM. In Task 4 we obtained Equal Error Rate (ERR) of 18.9% using Mel + DNN against 20.9% by using MFCCs + GMM. Therefore the DNN improves the baseline in Task 1, 3, and 4, although it is worse than the baseline in Task 2. This indicates that DNNs can be successful in many of these tasks, but may not always perform better than the baselines.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Kong, Qiuqiangq.kong@surrey.ac.ukUNSPECIFIED
Sobieraj, Iwonaiwona.sobieraj@surrey.ac.ukUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Plumbley, Markm.plumbley@surrey.ac.ukUNSPECIFIED
Date : 2016
Uncontrolled Keywords : Deep Neural Network (DNN), Acoustic Scene Classification (ASC), Acoustic Event Detection (AED), Audio Tagging
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 10 Feb 2017 17:41
Last Modified : 25 Aug 2017 11:47
URI: http://epubs.surrey.ac.uk/id/eprint/813518

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