Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Feature Learning Siamese Model for Intelligent Control of the Dynamic
  Range Compressor   In this paper, a siamese DNN model is proposed to learn the characteristics
of the audio dynamic range compressor (DRC). This facilitates an intelligent
control system that uses audio examples to configure the DRC, a widely used
non-linear audio signal conditioning technique in the areas of music
production, speech communication and broadcasting. Several alternative siamese
DNN architectures are proposed to learn feature embeddings that can
characterise subtle effects due to dynamic range compression. These models are
compared with each other as well as handcrafted features proposed in previous
work. The evaluation of the relations between the hyperparameters of DNN and
DRC parameters are also provided. The best model is able to produce a universal
feature embedding that is capable of predicting multiple DRC parameters
simultaneously, which is a significant improvement from our previous research.
The feature embedding shows better performance than handcrafted audio features
when predicting DRC parameters for both mono-instrument audio loops and
polyphonic music pieces.

20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics