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Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labeled Data

Sobieraj, Iwona, Kong, Qiuqiang and Plumbley, Mark (2017) Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labeled Data In: 25th European Signal Processing Conference, 2017 (EUSIPCO-2017), 28 Aug- 2 Sep 2017, Kos Island, Greece.

IwonaSobieraj_EUSIPCO2017.pdf - Accepted version Manuscript

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Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization (Masked NMF) approach for bird detection using weakly labeled data. During dictionary extraction we introduce a binary mask on the activation matrix. In that way we are able to control which parts of dictionary are used to reconstruct the training data. We compare our method with conventional NMF approaches and current state of the art methods. The proposed method outperforms the NMF baseline and offers a parsimonious model for bird detection on weakly labeled data. Moreover, to our knowledge, the proposed Masked NMF achieved the best result among non-deep learning methods on a test dataset used for the recent Bird Audio Detection Challenge.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 26 October 2017
Funders : EPSRC
DOI : 10.23919/EUSIPCO.2017.8081513
Copyright Disclaimer : First published in the Proceedings of the 25th European Signal Processing Conference (EUSIPCO-2017) in 2017, published by EURASIP. 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.
Depositing User : Melanie Hughes
Date Deposited : 08 Sep 2017 13:38
Last Modified : 11 Dec 2018 11:23

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