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#X msg 36 301 create \, 1;
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#X obj 119 274 outlet~;
#X obj 139 185 square 4;
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#X obj 61 165 separator;
#X obj 120 101 pix_video;
#X msg 186 64 dimen 640 480;
#X obj 26 36 block~ 2048;
#X msg 186 38 dimen 320 240;
#X msg 76 535 getprecision;
#X msg 93 696 getlearnrate;
#X msg 65 671 learnrate 0.2;
#X msg 424 459 getneurons;
#X msg 404 206 train;
#X obj 31 227 inlet~;
#X msg 65 647 learnrate 0.05;
#X msg 381 708 getmemory;
#X msg 361 639 memory 0;
#X msg 361 660 memory 1;
#X obj 61 252 pix_recNN;
#X text 296 49 <- input dimension;
#X obj 78 226 r \$0-recNN;
#X obj 62 564 s \$0-recNN;
#X msg 76 498 precision \$1;
#X floatatom 76 481 5 0 0 0 - - -;
#X text 42 335 precision:;
#X text 53 358 1: means every pixel is used in calculation;
#X text 53 372 2: only every second pixel;
#X text 53 386 ...;
#X obj 62 411 loadbang;
#X msg 407 401 neurons 2048;
#X msg 407 422 neurons 64;
#X obj 407 492 s \$0-recNN;
#X text 403 336 neurons:;
#X text 416 357 nr. of neurons used in the calculation;
#X text 415 370 (_MUST_ be the same as the buffersize !!!);
#X text 43 615 learnrate:;
#X obj 65 725 s \$0-recNN;
#X msg 361 681 memory 3;
#X obj 361 741 s \$0-recNN;
#X text 343 543 memory:;
#X text 356 565 this determines \, how much values from the past the
recurrent net considers in the calculation;
#X text 357 604 (be carefull with large values !!!);
#X msg 62 456 precision 1;
#X msg 62 436 precision 4;
#X obj 404 233 s \$0-recNN;
#X text 397 126 train:;
#X text 417 152 trains the neural net;
#X text 418 166 (the current video frame to;
#X text 425 178 the current audio block);
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#X msg 110 302 0 \, destroy;
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#X text 25 23 pix_recNN:;
#X text 24 57 pix_recNN is an instument/interface. This instrument
should be useful as a general experimental video interface to generate
audio. You can train the neural net with playing audio samples to specific
video frames in real-time. The main interest for me was not to train
the net exactly to reproduce these samples \, but to make experimental
sounds \, which are "between" all the trained samples.;
#X text 22 214 (but this version is unfinished - e.g. the training
algorithm must be tuned etc. - so it's only a very basic prototype...)
;
#X text 207 320 <- create gemwin;
#X obj 41 442 readsf~;
#X obj 41 401 openpanel;
#X msg 41 421 open \$1;
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-1;
#X text 67 379 <- load sample for training;
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1;
#X floatatom 206 414 5 0 0 0 - - -;
#X text 272 431 <- simple osc for training;
#X text 262 497 <- to train silence;
#X obj 85 463 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
-1;
#X text 216 541 <- audio/video work;
#X obj 90 684 dac~;
#X obj 90 659 *~;
#X obj 118 659 dbtorms;
#X floatatom 118 641 5 0 0 0 - - -;
#X text 168 638 <- outvol in dB;
#X text 22 170 pix_recNN uses a 2 layer recurrent neural net (for more
detailed info look at the source code.);
#X text 119 737 Georg Holzmann <grh@mur.at> \, 2004;
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