# A Hierarchical Deep Learning Approach for Minority Instrument Detection

arXiv: https://arxiv.org/abs/2506.21167 · HTML: https://mlsanity.com/paper/2506.21167/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition](https://mlsanity.com/paper/2107.07029/) (2021)
- [Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music](https://mlsanity.com/paper/1911.12505/) (2019)
- [Music Instrument Classification Reprogrammed](https://mlsanity.com/paper/2211.08379/) (2022)
- [Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms](https://mlsanity.com/paper/2102.06930/) (2021)
- [Exploring Isolated Musical Notes as Pre-training Data for Predominant Instrument Recognition in Polyphonic Music](https://mlsanity.com/paper/2306.08850/) (2023)

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

- [Persian Musical Instruments Classification Using Polyphonic Data Augmentation](https://mlsanity.com/paper/2511.05717/) (2025)

## Abstract (arXiv)

Identifying instrument activities within audio excerpts is vital in music information retrieval, with significant implications for music cataloging and discovery. Prior deep learning endeavors in musical instrument recognition have predominantly emphasized instrument classes with ample data availability. Recent studies have demonstrated the applicability of hierarchical classification in detecting instrument activities in orchestral music, even with limited fine-grained annotations at the instrument level. Based on the Hornbostel-Sachs classification, such a hierarchical classification system is evaluated using the MedleyDB dataset, renowned for its diversity and richness concerning various instruments and music genres. This work presents various strategies to integrate hierarchical structures into models and tests a new class of models for hierarchical music prediction. This study showcases more reliable coarse-level instrument detection by bridging the gap between detailed instrument identification and group-level recognition, paving the way for further advancements in this domain.