# Towards Human-in-the-Loop Onset Detection: A Transfer Learning Approach for Maracatu

arXiv: https://arxiv.org/abs/2507.04858 · HTML: https://mlsanity.com/paper/2507.04858/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [Transfer learning for music classification and regression tasks](https://mlsanity.com/paper/1703.09179/) (2017)
- [Few-Shot Drum Transcription in Polyphonic Music](https://mlsanity.com/paper/2008.02791/) (2020)
- [Score-informed syllable segmentation for a cappella singing voice with convolutional neural networks](https://mlsanity.com/paper/1707.03544/) (2017)

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

- [TimeCues Studio: A Workspace for Music Annotation and Algorithm Prototyping](https://mlsanity.com/paper/2609.10338/) (2026)

## Abstract (arXiv)

We explore transfer learning strategies for musical onset detection in the Afro-Brazilian Maracatu tradition, which features complex rhythmic patterns that challenge conventional models. We adapt two Temporal Convolutional Network architectures: one pre-trained for onset detection (intra-task) and another for beat tracking (inter-task). Using only 5-second annotated snippets per instrument, we fine-tune these models through layer-wise retraining strategies for five traditional percussion instruments. Our results demonstrate significant improvements over baseline performance, with F1 scores reaching up to 0.998 in the intra-task setting and improvements of over 50 percentage points in best-case scenarios. The cross-task adaptation proves particularly effective for time-keeping instruments, where onsets naturally align with beat positions. The optimal fine-tuning configuration varies by instrument, highlighting the importance of instrument-specific adaptation strategies. This approach addresses the challenges of underrepresented musical traditions, offering an efficient human-in-the-loop methodology that minimizes annotation effort while maximizing performance. Our findings contribute to more inclusive music information retrieval tools applicable beyond Western musical contexts.