PyTorch code coming soon...
Figure 4 : Figure 4. A network was trained only on the extreme
low and high pitches at the endpoints of the parameter
value range. During generation, the parameter value was
swept through untrained values between its lowest and its
highest and back again over 3 seconds. The result is this
continuously, though non-linearly varying pitch.
Figure 5 : Figure 5. The trained "Synth even" instrument controlled
with an arpeggio over the pitch range illustrates
the model's ability to respond quickly to pitch changes.
This image also shows that the untrained middle pitch
values are not synthesized as clearly as the trained values
at the extremities. Furthermore, the middle values contain
both even and odd harmonics, thus combining timbre
from each of the two trained instruments.
Figure 6 : Figure 6. When the network was trained on real data
with only two extreme values of pitch, pitch had a more
pronounced "stickiness" to extreme trained pitch values
showing a transition region without a glide as the pitch
parameter moves smoothly from low to high and back.
Figure 7 :
a.
b.
c.
Figure 7. a. The clarinet, trained on 13 chromatic notes
across an octave, generating a sweep with the pitch parameter
swept from low to high and back. b. The trumpet
playing the arpeggio pattern. c. A continuous forth-andback
sweep across the instrument parameter trained with
the trumpet and clarinet at its endpoint values.
