A Mamba-Based Model for Automatic Chord Recognition

2026-01-05

arXiv.org

AI Summary

This work addresses the trade-off between efficiency and accuracy in modeling long-range temporal dependencies for automatic chord recognition by proposing BMACE, the first model to introduce a bidirectional Mamba architecture to this task. Built upon selective structured state space models, BMACE integrates bidirectional temporal modeling with deep learning–based audio feature extraction to effectively capture long-range dependencies in musical signals. Experimental results demonstrate that BMACE achieves chord recognition accuracy comparable to state-of-the-art methods on standard benchmarks while substantially reducing both model parameters and computational overhead, thereby unifying high accuracy with high efficiency.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

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

Problem

• Automatic Chord Recognition
• Mamba
• Temporal Dependencies
• Efficient Model
• State-Space Models

Innovation

• Mamba
• structured state-space model
• automatic chord recognition
• bidirectional modeling
• efficient neural network