# Unsupervised classification to improve the quality of a bird song recording dataset arXiv: https://arxiv.org/abs/2302.07560 ยท HTML: https://mlsanity.com/paper/2302.07560/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 87 references that are in the map. - First posted on arXiv 15 Feb 2023. ## Builds on (most cited first, 21 of 21) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018) - [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019) - [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014) - [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017) - [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016) - [Green AI](https://mlsanity.com/paper/1907.10597/) (2019) - [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019) - [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019) - [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016) - [Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels](https://mlsanity.com/paper/1905.05040/) (2019) - [Deep learning with noisy labels: exploring techniques and remedies in medical image analysis](https://mlsanity.com/paper/1912.02911/) (2019) - [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017) - [Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey](https://mlsanity.com/paper/1912.05170/) (2019) - [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021) - [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022) - [A Data-Centric Approach for Training Deep Neural Networks with Less Data](https://mlsanity.com/paper/2110.03613/) (2021) - [Improving Bird Classification with Unsupervised Sound Separation](https://mlsanity.com/paper/2110.03209/) (2021) - [Few-shot bioacoustic event detection at the DCASE 2022 challenge](https://mlsanity.com/paper/2207.07911/) (2022) - [You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection](https://mlsanity.com/paper/2109.00962/) (2021) - [Parsing Birdsong with Deep Audio Embeddings](https://mlsanity.com/paper/2108.09203/) (2021) ## Built on by (most cited first, 10 of 10) - [BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics](https://mlsanity.com/paper/2403.10380/) (2024) - [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025) - [AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring](https://mlsanity.com/paper/2307.06860/) (2023) - [AudioProtoPNet: An interpretable deep learning model for bird sound classification](https://mlsanity.com/paper/2404.10420/) (2024) - [Synthetic data enables context-aware bioacoustic sound event detection](https://mlsanity.com/paper/2503.00296/) (2025) - [bacpipe: a Python package to make bioacoustic deep learning models accessible](https://mlsanity.com/paper/2604.11560/) (2026) - [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026) - [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) - [DeepForestSound: a multi-species automatic detector for passive acoustic monitoring in African tropical forests, a case study in Kibale National Park](https://mlsanity.com/paper/2604.08087/) (2026) ## Abstract (arXiv) Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort.