Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Phase reconstruction based on recurrent phase unwrapping with deep
  neural networks   Phase reconstruction, which estimates phase from a given amplitude
spectrogram, is an active research field in acoustical signal processing with
many applications including audio synthesis. To take advantage of rich
knowledge from data, several studies presented deep neural network (DNN)--based
phase reconstruction methods. However, the training of a DNN for phase
reconstruction is not an easy task because phase is sensitive to the shift of a
waveform. To overcome this problem, we propose a DNN-based two-stage phase
reconstruction method. In the proposed method, DNNs estimate phase derivatives
instead of phase itself, which allows us to avoid the sensitivity problem.
Then, phase is recursively estimated based on the estimated derivatives, which
is named recurrent phase unwrapping (RPU). The experimental results confirm
that the proposed method outperformed the direct phase estimation by a DNN.

13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex