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A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

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Published on: November 26, 2012

NIPS4Bplus: a richly annotated birdsong audio dataset.

Veronica Morfi¹, Yves Bas^2,3, Hanna Pamuła⁴

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

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

Introducing NIPS4Bplus, the first richly annotated birdsong dataset. This dataset enables advanced ecoacoustic research and bird monitoring by providing species tags and temporal annotations for bird vocalizations.

Keywords:

Audio dataset Audio signal processing Bioacoustics Bioinformatics Bird vocalisations Ecoacoustics Ecosystems Rich annotations

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Area of Science:

Bioacoustics
Computational Ecology
Machine Learning

Background:

Birdsong detection and classification research is limited by the scarcity of fully annotated audio recordings.
Existing datasets often lack detailed temporal information or species-specific tags for bird vocalizations.

Purpose of the Study:

To introduce NIPS4Bplus, a novel, richly annotated birdsong audio dataset.
To provide a comprehensive resource for advancing ecoacoustic research and bird monitoring applications.

Main Methods:

Compilation of birdsong recordings with active species tags.
Inclusion of precise temporal annotations for bird vocalizations within the recordings.
Statistical analysis of dataset characteristics and example use cases.

Main Results:

NIPS4Bplus is the first dataset offering detailed species and temporal annotations for birdsong.
The dataset includes statistical insights into recording properties, species tags, and temporal data.
Demonstration of potential applications in ecoacoustic tasks.

Conclusions:

NIPS4Bplus addresses the critical need for annotated data in birdsong analysis.
The dataset facilitates the development of improved models for bird population monitoring and species classification.
NIPS4Bplus is poised to significantly contribute to the fields of bioacoustics and computational ecology.