使用深度神经网络的大词汇量自动和弦估计：设计框架，系统变化和限制
Large Vocabulary Automatic Chord Estimation Using Deep Neural Nets: Design Framework, System Variations and Limitations
摘要：
在本文中，我们提出了一个新的系统设计框架，大词汇自动和弦估计。我们的方法是基于传统的序列分割过程和深度学习和弦分类技术的集成。我们系统地探索了所提出的框架的一系列参数的设计空间，即深度神经网络、网络配置、输入特征表示、分段平铺方案和训练数据大小。实验结果表明，在三个提出的深度神经网络和基线模型中，基于递归神经网络的系统具有最好的平均和弦质量准确度，显著优于其他考虑的模型。此外，我们的偏差方差分析已经确定了玻璃天花板作为一个潜在的障碍，未来的大词汇量自动和弦估计系统的改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
