NIPS4Bplus: a richly annotated birdsong audio dataset-Reference-Cited by-同舟云学术

NIPS4Bplus: a richly annotated birdsong audio dataset

Published:2019-10-07 Issue: Volume:5 Page:e223
ISSN:2376-5992
Container-title:PeerJ Computer Science
language:en
Short-container-title:

Author:

Morfi Veronica¹,Bas Yves²³,Pamuła Hanna⁴,Glotin Hervé⁵,Stowell Dan¹

Affiliation:

1. Machine Listening Lab, Centre for Digital Music (C4DM), Department of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom

2. Centre d’Ecologie et des Sciences de la Conservation (CESCO), Muséum National d’Histoire Naturelle, CNRS, Sorbonne Université, Paris, France

3. Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, Montpellier, France

4. Department of Mechanics and Vibroacoustics, AGH University of Science and Technology, Kraków, Poland

5. CNRS, LIS, DYNI team, SABIOD, Université de Toulon (UTLN), Aix Marseille Université (AMU), Marseille, France

Abstract

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.

Funder

EPSRC fellowship

AGH-UST Dean’s

NIPS4B challenge

EADM MaDICS CNRS

Publisher

PeerJ

Subject

General Computer Science

Link

https://peerj.com/articles/cs-223.pdf

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