A Mamba-Based Model for Automatic Chord Recognition
On the map: State-space sequence models (S4, Mamba) → Speech and audio SSMs
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
Key facts. No citing papers in the map yet (data through 7 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.
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9 of 18 references.
Closest ancestors · 3
Same direction, newest first
- 2024A Two-Stage Band-Split Mamba-2 Network For Music Separation10 Sep
- 2024MusicMamba: A Dual-Feature Modeling Approach for Generating Chinese Traditional Music with Modal Precision4 Sep
- 2024Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation27 Mar
Other references · 6
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2024Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakerscited by 28
- 2019A Bi-directional Transformer for Musical Chord Recognitioncited by 19
- 2016A Fully Convolutional Deep Auditory Model for Musical Chord Recognitioncited by 7
- 2017Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representationscited by 6
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