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+#N canvas 871 74 498 738 10;
+#X obj 28 237 gemwin;
+#X msg 28 211 create \, 1;
+#N canvas 463 0 765 790 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 text 296 49 <- input dimension;
+#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 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 msg 62 456 precision 1;
+#X msg 62 436 precision 4;
+#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 obj 61 252 pix_linNN;
+#X text 346 592 save/load;
+#X text 359 614 saves/load the actual trained net to/from a file;
+#X msg 440 684 load net.dat;
+#X msg 440 664 save net.dat;
+#X obj 78 226 r \$0-linNN;
+#X obj 404 233 s \$0-linNN;
+#X obj 62 564 s \$0-linNN;
+#X obj 407 492 s \$0-linNN;
+#X obj 65 725 s \$0-linNN;
+#X obj 440 723 s \$0-linNN;
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+#X restore 87 492 pd pix2sig_stuff~;
+#X msg 102 212 0 \, destroy;
+#X obj 114 537 unsig~;
+#X obj 204 382 osc~ 440;
+#X obj 203 406 *~;
+#X obj 235 406 tgl 15 0 empty empty empty 0 -6 0 8 -262144 -1 -1 0
+1;
+#X obj 205 446 sig~ 0;
+#X floatatom 115 558 8 0 0 0 - - -;
+#X text 199 230 <- create gemwin;
+#X obj 39 392 readsf~;
+#X obj 39 351 openpanel;
+#X msg 39 371 open \$1;
+#X obj 39 330 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 65 329 <- load sample for training;
+#X obj 120 367 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0
+1;
+#X floatatom 204 364 5 0 0 0 - - -;
+#X text 270 381 <- simple osc for training;
+#X text 260 447 <- to train silence;
+#X obj 83 413 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 214 491 <- audio/video work;
+#X obj 88 634 dac~;
+#X obj 88 609 *~;
+#X obj 116 609 dbtorms;
+#X floatatom 116 591 5 0 0 0 - - -;
+#X text 166 588 <- outvol in dB;
+#X text 110 703 Georg Holzmann <grh@mur.at> \, 2004;
+#X text 24 23 pix_linNN:;
+#X text 22 58 (see also pix_recNN !!!);
+#X text 24 90 pix_linNN~ calculates an audio signal out of a video
+frame with a linear neural network \, which can be trained.;
+#X text 24 124 The network has one neuron per audio sample: this neuron
+has three inputs (a RGB-signal) \, a weight vector for each of the
+inputs \, a bias value and a linear output function.;
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