# Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition

arXiv: https://arxiv.org/abs/2107.07029 · HTML: https://mlsanity.com/paper/2107.07029/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks](https://mlsanity.com/paper/1912.09393/) (2019)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Hierarchical Image Classification using Entailment Cone Embeddings](https://mlsanity.com/paper/2004.03459/) (2020)
- [Hierarchical Multi Task Learning With CTC](https://mlsanity.com/paper/1807.07104/) (2018)
- [An Analysis of Hierarchical Text Classification Using Word Embeddings](https://mlsanity.com/paper/1809.01771/) (2018)
- [Deep convolutional networks on the pitch spiral for musical instrument recognition](https://mlsanity.com/paper/1605.06644/) (2016)
- [Hierarchical Multitask Learning for CTC-based Speech Recognition](https://mlsanity.com/paper/1807.06234/) (2018)
- [An Attention Mechanism for Musical Instrument Recognition](https://mlsanity.com/paper/1907.04294/) (2019)
- [Few-shot acoustic event detection via meta-learning](https://mlsanity.com/paper/2002.09143/) (2020)
- [Deep convolutional neural networks for predominant instrument recognition in polyphonic music](https://mlsanity.com/paper/1605.09507/) (2016)
- [Multitask learning for frame-level instrument recognition](https://mlsanity.com/paper/1811.01143/) (2018)
- [Frame-level Instrument Recognition by Timbre and Pitch](https://mlsanity.com/paper/1806.09587/) (2018)
- [Few-Shot Drum Transcription in Polyphonic Music](https://mlsanity.com/paper/2008.02791/) (2020)
- [Extended playing techniques: The next milestone in musical instrument recognition](https://mlsanity.com/paper/1808.09730/) (2018)
- [Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music](https://mlsanity.com/paper/1911.12505/) (2019)
- [Hierarchical learning for DNN-based acoustic scene classification](https://mlsanity.com/paper/1607.03682/) (2016)

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

- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Rank-based loss for learning hierarchical representations](https://mlsanity.com/paper/2110.05941/) (2021)
- [Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio](https://mlsanity.com/paper/2211.07951/) (2022)
- [Learning Unsupervised Hierarchies of Audio Concepts](https://mlsanity.com/paper/2207.11231/) (2022)
- [Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review](https://mlsanity.com/paper/2408.10330/) (2024)
- [MIRFLEX: Music Information Retrieval Feature Library for Extraction](https://mlsanity.com/paper/2411.00469/) (2024)
- [HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones](https://mlsanity.com/paper/2303.07538/) (2023)
- [Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data](https://mlsanity.com/paper/2307.12576/) (2023)
- [Hybrid Losses for Hierarchical Embedding Learning](https://mlsanity.com/paper/2501.12796/) (2025)
- [A Hierarchical Deep Learning Approach for Minority Instrument Detection](https://mlsanity.com/paper/2506.21167/) (2025)
- [Representation Learning for the Automatic Indexing of Sound Effects Libraries](https://mlsanity.com/paper/2208.09096/) (2022)
- [Learning Music Audio Representations With Limited Data](https://mlsanity.com/paper/2505.06042/) (2025)
- [Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training](https://mlsanity.com/paper/2506.17499/) (2025)
- [Extending Music Annotation Schemas: Zero-Shot Prediction or Few-Shot Adaptation?](https://mlsanity.com/paper/2610.06920/) (2026)

## Abstract (arXiv)

Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data. In this work, we exploit hierarchical relationships between instruments in a few-shot learning setup to enable classification of a wider set of musical instruments, given a few examples at inference. We apply a hierarchical loss function to the training of prototypical networks, combined with a method to aggregate prototypes hierarchically, mirroring the structure of a predefined musical instrument hierarchy. These extensions require no changes to the network architecture and new levels can be easily added or removed. Compared to a non-hierarchical few-shot baseline, our method leads to a significant increase in classification accuracy and significant decrease mistake severity on instrument classes unseen in training.