# NIPS4Bplus: a richly annotated birdsong audio dataset

arXiv: https://arxiv.org/abs/1811.02275 · HTML: https://mlsanity.com/paper/1811.02275/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 14 of 14)

- [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024)
- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics](https://mlsanity.com/paper/2406.01253/) (2024)
- [Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset](https://mlsanity.com/paper/2207.10664/) (2022)
- [AudioProtoPNet: An interpretable deep learning model for bird sound classification](https://mlsanity.com/paper/2404.10420/) (2024)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos](https://mlsanity.com/paper/2501.08931/) (2025)
- [Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset](https://mlsanity.com/paper/2207.05393/) (2022)
- [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026)
- [Merlin L48 Spectrogram Dataset](https://mlsanity.com/paper/2511.00252/) (2025)
- [NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe](https://mlsanity.com/paper/2412.03633/) (2024)
- [Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification](https://mlsanity.com/paper/2507.13727/) (2025)
- [A strongly annotated passive acoustic dataset for tropical bird monitoring](https://mlsanity.com/paper/2605.20578/) (2026)
- [Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types](https://mlsanity.com/paper/2607.03304/) (2026)

## Abstract (arXiv)

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.