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Joint Detection and Classification Convolutional Neural Network on Weakly Labelled Bird Audio Detection

Kong, Qiuqiang, Xu, Yong and Plumbley, Mark (2017) Joint Detection and Classification Convolutional Neural Network on Weakly Labelled Bird Audio Detection In: 25th European Signal Processing Conference (EUSIPCO) 2017, Aug 28 - Sep 2 2017, Kos Island, Greece.

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Joint detection and classification convolutional neural network (JDC-CNN) on weakly labelled bird audio data (BAD)_v1.1_IEEEformat.pdf - Accepted version Manuscript

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Abstract

Bird audio detection (BAD) aims to detect whether there is a bird call in an audio recording or not. One difficulty of this task is that the bird sound datasets are weakly labelled, that is only the presence or absence of a bird in a recording is known, without knowing when the birds call. We propose to apply joint detection and classification (JDC) model on the weakly labelled data (WLD) to detect and classify an audio clip at the same time. First, we apply VGG like convolutional neural network (CNN) on mel spectrogram as baseline. Then we propose a JDC-CNN model with VGG as a classifier and CNN as a detector. We report the denoising method including optimally-modified log-spectral amplitude (OM-LSA), median filter and spectral spectrogram will worse the classification accuracy on the contrary to previous work. JDC-CNN can predict the time stamps of the events from weakly labelled data, so is able to do sound event detection from WLD. We obtained area under curve (AUC) of 95.70% on the development data and 81.36% on the unseen evaluation data, which is nearly comparable to the baseline CNN model.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Kong, Qiuqiangq.kong@surrey.ac.ukUNSPECIFIED
Xu, Yongyong.xu@surrey.ac.ukUNSPECIFIED
Plumbley, Markm.plumbley@surrey.ac.ukUNSPECIFIED
Date : 22 September 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 22 Jun 2017 12:38
Last Modified : 22 Jun 2017 12:38
URI: http://epubs.surrey.ac.uk/id/eprint/841463

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