Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single-channel speech separation using Soft-minimum Permutation
  Invariant Training   The goal of speech separation is to extract multiple speech sources from a
single microphone recording. Recently, with the advancement of deep learning
and availability of large datasets, speech separation has been formulated as a
supervised learning problem. These approaches aim to learn discriminative
patterns of speech, speakers, and background noise using a supervised learning
algorithm, typically a deep neural network. A long-lasting problem in
supervised speech separation is finding the correct label for each separated
speech signal, referred to as label permutation ambiguity. Permutation
ambiguity refers to the problem of determining the output-label assignment
between the separated sources and the available single-speaker speech labels.
Finding the best output-label assignment is required for calculation of
separation error, which is later used for updating parameters of the model.
Recently, Permutation Invariant Training (PIT) has been shown to be a promising
solution in handling the label ambiguity problem. However, the overconfident
choice of the output-label assignment by PIT results in a sub-optimal trained
model. In this work, we propose a probabilistic optimization framework to
address the inefficiency of PIT in finding the best output-label assignment.
Our proposed method entitled trainable Soft-minimum PIT is then employed on the
same Long-Short Term Memory (LSTM) architecture used in Permutation Invariant
Training (PIT) speech separation method. The results of our experiments show
that the proposed method outperforms conventional PIT speech separation
significantly (p-value $ < 0.01$) by +1dB in Signal to Distortion Ratio (SDR)
and +1.5dB in Signal to Interference Ratio (SIR).

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The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc