# Exploring Meta Information for Audio-based Zero-shot Bird Classification

arXiv: https://arxiv.org/abs/2309.08398 · HTML: https://mlsanity.com/paper/2309.08398/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [Wav2CLIP: Learning Robust Audio Representations From CLIP](https://mlsanity.com/paper/2110.11499/) (2021)
- [Learning Deep Representations of Fine-grained Visual Descriptions](https://mlsanity.com/paper/1605.05395/) (2016)
- [Label-Embedding for Image Classification](https://mlsanity.com/paper/1503.08677/) (2015)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [Zero-Shot Audio Classification via Semantic Embeddings](https://mlsanity.com/paper/2011.12133/) (2020)
- [Fine-Grained Zero-Shot Learning with DNA as Side Information](https://mlsanity.com/paper/2109.14133/) (2021)
- [Zero-Shot Audio Classification using Image Embeddings](https://mlsanity.com/paper/2206.04984/) (2022)

## Built on by (most cited first, 4 of 4)

- [Multi-label Zero-Shot Audio Classification with Temporal Attention](https://mlsanity.com/paper/2409.00408/) (2024)
- [An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon](https://mlsanity.com/paper/2504.16276/) (2025)
- [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026)
- [CoarseSoundNet: Building a reliable model for ecological soundscape analysis](https://mlsanity.com/paper/2605.21143/) (2026)

## Abstract (arXiv)

Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is still an issue for rare and underrepresented species. This study investigates how meta-information can improve zero-shot audio classification, utilising bird species as an example case study due to the availability of rich and diverse meta-data. We investigate three different sources of metadata: textual bird sound descriptions encoded via (S)BERT, functional traits (AVONET), and bird life-history (BLH) characteristics. As audio features, we extract audio spectrogram transformer (AST) embeddings and project them to the dimension of the auxiliary information by adopting a single linear layer. Then, we employ the dot product as compatibility function and a standard zero-shot learning ranking hinge loss to determine the correct class. The best results are achieved by concatenating the AVONET and BLH features attaining a mean unweighted F1-score of .233 over five different test sets with 8 to 10 classes.