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+#N canvas 871 74 498 783 10;
+#X obj 36 327 gemwin;
+#X msg 36 301 create \, 1;
+#N canvas 75 72 765 790 pix2sig_stuff~ 0;
+#X obj 120 35 gemhead;
+#X obj 120 132 pix_texture;
+#X obj 119 274 outlet~;
+#X obj 139 185 square 4;
+#X obj 139 163 separator;
+#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);
+#X connect 0 0 6 0;
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+#X restore 89 542 pd pix2sig_stuff~;
+#X msg 110 302 0 \, destroy;
+#X obj 116 587 unsig~;
+#X obj 206 432 osc~ 440;
+#X obj 205 456 *~;
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+1;
+#X obj 207 496 sig~ 0;
+#X floatatom 117 608 8 0 0 0 - - -;
+#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;
+#X obj 41 380 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 67 379 <- load sample for training;
+#X obj 122 417 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0
+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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