# Few-shot Long-Tailed Bird Audio Recognition

arXiv: https://arxiv.org/abs/2206.11260 · HTML: https://mlsanity.com/paper/2206.11260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [High-Performance Large-Scale Image Recognition Without Normalization](https://mlsanity.com/paper/2102.06171/) (2021)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection](https://mlsanity.com/paper/2203.15296/) (2022)
- [Learning Sound Event Classifiers from Web Audio with Noisy Labels](https://mlsanity.com/paper/1901.01189/) (2019)
- [Exploring Vision Transformers for Fine-grained Classification](https://mlsanity.com/paper/2106.10587/) (2021)
- [Sound Event Detection with Depthwise Separable and Dilated Convolutions](https://mlsanity.com/paper/2002.00476/) (2020)
- [An open dataset for research on audio field recording archives: freefield1010](https://mlsanity.com/paper/1309.5275/) (2013)
- [Multi-attention Networks for Temporal Localization of Video-level Labels](https://mlsanity.com/paper/1911.06866/) (2019)
- [Robust sound event detection in bioacoustic sensor networks](https://mlsanity.com/paper/1905.08352/) (2019)
- [Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022](https://mlsanity.com/paper/2206.04805/) (2022)
- [Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solution](https://mlsanity.com/paper/2107.04878/) (2021)
- [Recognizing bird species in diverse soundscapes under weak supervision](https://mlsanity.com/paper/2107.07728/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Brief Overview of AI Governance for Responsible Machine Learning Systems](https://mlsanity.com/paper/2211.13130/) (2022)

## Abstract (arXiv)

It is easier to hear birds than see them. However, they still play an essential role in nature and are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Deep Neural Networks allow us to process audio data to detect and classify birds. This technology can assist researchers in monitoring bird populations and biodiversity. We propose a sound detection and classification pipeline to analyze complex soundscape recordings and identify birdcalls in the background. Our method learns from weak labels and few data and acoustically recognizes the bird species. Our solution achieved 18th place of 807 teams at the BirdCLEF 2022 Challenge hosted on Kaggle.