# A Mamba-Based Model for Automatic Chord Recognition

arXiv: https://arxiv.org/abs/2601.02101 · HTML: https://mlsanity.com/paper/2601.02101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Placed in

- State-space sequence models (S4, Mamba) / [Speech and audio SSMs](https://mlsanity.com/ssm/speech-and-audio-ssms/)

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation](https://mlsanity.com/paper/2403.18257/) (2024)
- [Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers](https://mlsanity.com/paper/2403.07675/) (2024)
- [A Bi-directional Transformer for Musical Chord Recognition](https://mlsanity.com/paper/1907.02698/) (2019)
- [A Fully Convolutional Deep Auditory Model for Musical Chord Recognition](https://mlsanity.com/paper/1612.05082/) (2016)
- [Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations](https://mlsanity.com/paper/1706.09552/) (2017)
- [MusicMamba: A Dual-Feature Modeling Approach for Generating Chinese Traditional Music with Modal Precision](https://mlsanity.com/paper/2409.02421/) (2024)
- [A Two-Stage Band-Split Mamba-2 Network For Music Separation](https://mlsanity.com/paper/2409.06245/) (2024)

## Abstract (arXiv)

In this work, we propose a new efficient solution, which is a Mamba-based model named BMACE (Bidirectional Mamba-based network, for Automatic Chord Estimation), which utilizes selective structured state-space models in a bidirectional Mamba layer to effectively model temporal dependencies. Our model achieves high prediction performance comparable to state-of-the-art models, with the advantage of requiring fewer parameters and lower computational resources