Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Blind Source Separation Framework Towards Maximum
  Signal-To-Interference Ratio   This letter proposes a new blind source separation (BSS) framework termed
minimum variance independent component analysis (MVICA), which can potentially
achieve the maximum output signal-to-interference ratio (SIR) while also
allowing more flexibility in real implementations. The statistical independence
assumption has been the foundation of the most dominant BSS techniques in
recent decades. However, this assumption does not always hold true and the
accurate probabilistic modeling of source is inherently difficult. To overcome
these limitations and improve the separation performance, the MVICA framework
is rigorously derived by optimizing the design of these independence-based BSS
algorithms with the maximum SIR criterion. A deep neural network-supported
implementation of MVICA is subsequently described. Experimental results under
various conditions show the superiority of MVICA over the state-of-the-art BSS
algorithms, in terms of not only SIR but also signal-to-distortion ratio and
automatic speech recognition rate.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
16
The categories of this paper are: stat