| Name |
Range |
Default |
Valid templates |
Description |
| pan |
0.000 to 1.000 |
0.500 |
deterministic, transient, background, raw, loop,
timeline, mixed bag |
panning between left (0.0) and right (1.0)
speakers |
| gain |
0.100 to 10.000 |
1.000 |
deterministic, transient, background, raw, loop,
timeline, mixed bag |
volume of selected template |
| freq-warp |
0.010 to 100.000 |
1.000 |
deterministic, transient, raw, loop |
frequency warp amount (transients/raws do not
attain full range) |
| time-stretch |
0.010 to 100.000 |
1.000 |
deterministic, transient, raw, loop |
time stretch amount (transients/raws do not attain
full range) |
| periodicity |
0.000 to 1.000 |
0.500 |
loop, mixed bag |
periodicity at which a template is repeated |
| density |
0.001 to 1000.000 |
0.500 |
loop, mixed bag |
average number of times a template is
repeated per second |
| randomness |
1.000 to 3.000 |
2.000 |
loop, mixed bag |
range for random freq-warp, time-stretch, pan
and gain transformations on template; if the specified value
of one of these parameters is P, its range is [P/randomness,
P*randomness] |
| likelihood |
0 to 20 |
1 |
mixed bag |
how often a template is played compared to
other templates in the bag |
| play once |
true, false |
false |
mixed bag |
whether a template in the bag is played once
or repeats |
randomness
(wavelet tree) |
0.001 to 1.000 |
0.250 |
background |
error in learning wavelet tree coefficients;
P (percentage) parameter in original Dubnov et. al. paper |
| similarity |
0.000 to 1.000 |
0.300 |
background |
fraction of predecessors considered at each
level of wavelet tree learning |
| start-level |
1 to 15 |
1 |
background |
level at which wavelet tree learning begins |
| stop-level |
1 to 15 |
9 |
background |
level at which wavelet tree learning stops
(for optimization) |
| total-levels |
1 to 18 |
13 |
background |
total number of levels in wavelet tree, for
lengthy sound files; 2^total-levels is the number of samples
analyzed in one segment |
| analysis order |
true, false |
true |
background |
whether the learning algorithm considers
ancestors (as in original algorithm, so true) or predecessors (false)
first |
| more random |
true, false |
false |
background |
whether to randomly swap coefficients in the
first level of learning instead of copying them directly, for
slightly more structural randomness |
| duration |
1.000 ms to 100.000 weeks |
1.000 minute |
timeline |
duration of timeline |