Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single-Channel Speech Separation with Auxiliary Speaker Embeddings   We present a novel source separation model to decompose asingle-channel
speech signal into two speech segments belonging to two different speakers. The
proposed model is a neural network based on residual blocks, and uses learnt
speaker embeddings created from additional clean context recordings of the two
speakers as input to assist in attributing the different time-frequency bins to
the two speakers. In experiments, we show that the proposed model yields good
performance in the source separation task, and outperforms the state-of-the-art
baselines. Specifically, separating speech from the challenging VoxCeleb
dataset, the proposed model yields 4.79dB signal-to-distortion ratio, 8.44dB
signal-to-artifacts ratio and 7.11dB signal-to-interference ratio.

17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg