Large Vocabulary Automatic Chord Estimation Using Deep Neural Nets: Design Framework, System Variations and Limitations
This work addresses the problem of automatic chord estimation for music analysis, but it is incremental as it builds on existing deep learning techniques within a new framework.
The authors tackled large vocabulary automatic chord estimation by proposing a new system design framework that integrates traditional sequence segmentation with deep learning classification, and found that a recurrent neural network-based system achieved the best average chord quality accuracy, significantly outperforming other models.
In this paper, we propose a new system design framework for large vocabulary automatic chord estimation. Our approach is based on an integration of traditional sequence segmentation processes and deep learning chord classification techniques. We systematically explore the design space of the proposed framework for a range of parameters, namely deep neural nets, network configurations, input feature representations, segment tiling schemes, and training data sizes. Experimental results show that among the three proposed deep neural nets and a baseline model, the recurrent neural network based system has the best average chord quality accuracy that significantly outperforms the other considered models. Furthermore, our bias-variance analysis has identified a glass ceiling as a potential hindrance to future improvements of large vocabulary automatic chord estimation systems.