Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large Vocabulary Automatic Chord Estimation Using Deep Neural Nets:
  Design Framework, System Variations and Limitations   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.

27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ