Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-End Neural Speech Coding for Real-Time Communications   Deep-learning based methods have shown their advantages in audio coding over
traditional ones but limited attention has been paid on real-time
communications (RTC). This paper proposes the TFNet, an end-to-end neural
speech codec with low latency for RTC. It takes an encoder-temporal
filtering-decoder paradigm that has seldom been investigated in audio coding.
An interleaved structure is proposed for temporal filtering to capture both
short-term and long-term temporal dependencies. Furthermore, with end-to-end
optimization, the TFNet is jointly optimized with speech enhancement and packet
loss concealment, yielding a one-for-all network for three tasks. Both
subjective and objective results demonstrate the efficiency of the proposed
TFNet.

8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math