Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditioning a Recurrent Neural Network to synthesize musical instrument
  transients   A recurrent Neural Network (RNN) is trained to predict sound samples based on
audio input augmented by control parameter information for pitch, volume, and
instrument identification. During the generative phase following training,
audio input is taken from the output of the previous time step, and the
parameters are externally controlled allowing the network to be played as a
musical instrument. Building on an architecture developed in previous work, we
focus on the learning and synthesis of transients - the temporal response of
the network during the short time (tens of milliseconds) following the onset
and offset of a control signal. We find that the network learns the particular
transient characteristics of two different synthetic instruments, and
furthermore shows some ability to interpolate between the characteristics of
the instruments used in training in response to novel parameter settings. We
also study the behaviour of the units in hidden layers of the RNN using various
visualisation techniques and find a variety of volume-specific response
characteristics.

11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math