# LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging

arXiv: https://arxiv.org/abs/2409.11264 · HTML: https://mlsanity.com/paper/2409.11264/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 56 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [Neural Voice Cloning with a Few Samples](https://mlsanity.com/paper/1802.06006/) (2018)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [Learning to Remember Rare Events](https://mlsanity.com/paper/1703.03129/) (2017)
- [musicnn: Pre-trained convolutional neural networks for music audio tagging](https://mlsanity.com/paper/1909.06654/) (2019)
- [A Foundation Model for Music Informatics](https://mlsanity.com/paper/2311.03318/) (2023)
- [Evaluation of CNN-based Automatic Music Tagging Models](https://mlsanity.com/paper/2006.00751/) (2020)
- [Multi-Format Contrastive Learning of Audio Representations](https://mlsanity.com/paper/2103.06508/) (2021)
- [LaSO: Label-Set Operations networks for multi-label few-shot learning](https://mlsanity.com/paper/1902.09811/) (2019)
- [Transfer learning for music classification and regression tasks](https://mlsanity.com/paper/1703.09179/) (2017)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples](https://mlsanity.com/paper/1805.06556/) (2018)
- [Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms](https://mlsanity.com/paper/1703.01789/) (2017)
- [Meta-Learning for Multi-Label Few-Shot Classification](https://mlsanity.com/paper/2110.13494/) (2021)
- [Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition](https://mlsanity.com/paper/2107.07029/) (2021)
- [Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms](https://mlsanity.com/paper/1710.10451/) (2017)
- [Multi-Label Few-Shot Learning for Aspect Category Detection](https://mlsanity.com/paper/2105.14174/) (2021)
- [Few-Shot Musical Source Separation](https://mlsanity.com/paper/2205.01273/) (2022)
- [A Dataset for Greek Traditional and Folk Music: Lyra](https://mlsanity.com/paper/2211.11479/) (2022)
- [Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition](https://mlsanity.com/paper/2212.08952/) (2022)

## Built on by (most cited first, 7 of 7)

- [Universal Music Representations? Evaluating Foundation Models on World Music Corpora](https://mlsanity.com/paper/2506.17055/) (2025)
- [CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning](https://mlsanity.com/paper/2506.17818/) (2025)
- [Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes](https://mlsanity.com/paper/2607.01297/) (2026)
- [Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring](https://mlsanity.com/paper/2506.13909/) (2025)
- [Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training](https://mlsanity.com/paper/2506.17499/) (2025)
- [Semantic F1 Scores: Fair Evaluation Under Fuzzy Class Boundaries](https://mlsanity.com/paper/2509.21633/) (2025)
- [Extending Music Annotation Schemas: Zero-Shot Prediction or Few-Shot Adaptation?](https://mlsanity.com/paper/2610.06920/) (2026)

## Abstract (arXiv)

We introduce Label-Combination Prototypical Networks (LC-Protonets) to address the problem of multi-label few-shot classification, where a model must generalize to new classes based on only a few available examples. Extending Prototypical Networks, LC-Protonets generate one prototype per label combination, derived from the power set of labels present in the limited training items, rather than one prototype per label. Our method is applied to automatic audio tagging across diverse music datasets, covering various cultures and including both modern and traditional music, and is evaluated against existing approaches in the literature. The results demonstrate a significant performance improvement in almost all domains and training setups when using LC-Protonets for multi-label classification. In addition to training a few-shot learning model from scratch, we explore the use of a pre-trained model, obtained via supervised learning, to embed items in the feature space. Fine-tuning improves the generalization ability of all methods, yet LC-Protonets achieve high-level performance even without fine-tuning, in contrast to the comparative approaches. We finally analyze the scalability of the proposed method, providing detailed quantitative metrics from our experiments. The implementation and experimental setup are made publicly available, offering a benchmark for future research.