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+#X text 166 24 INVERSE MODELLING;
+#X text 34 66 Now the signal source is split up into an unknown channel
+and the desired signal is delayed added into the adaption process.
+;
+#X text 32 115 The goal is to adapt the channel \, so that the overal
+system has a flat frequency response:;
+#X text 32 177 So the overal system is a delayed version of the input
+signal.;
+#X text 47 312 1 select a channel (dummy system or a real room - so
+you will need a loudspeaker and a microphone);
+#X text 142 154 H_ch(z) * H_eq(z) = z^-M;
+#X text 45 381 3 train the system \, to get H_eq(z) (use speech- or
+music samples to train the real room);
+#X text 46 414 4 use the euqlized system (in case of a real room you
+should have a nearly flat frequency response in that room);
+#X text 30 290 Usage of the patch:;
+#X text 47 347 2 measure the latency of your system (for real room:
+don't forget to turn on the volumes for micro and speaker);
+#X text 60 239 a) dummy system: a simple bandpass filter with a delay
+;
+#X text 62 253 b) real room: loudspeaker - a room - micro;
+#X text 29 221 You can select between two different channels:;
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+#X text 152 22 OBSERVATIONS;
+#X text 32 95 The magnitude of the frequency response can be equalized.
+;
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+#X text 16 128 a real room:;
+#X text 33 148 The magnitude of the frequency can only be equalized
+if you have an input signal with high energy.;
+#X text 31 181 The adaptation has problems with noise as input signal
+\, because noise is totally uncorrelated \, so you to measure the latency
+very precise.;
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+signals such as music or speech samples.;
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+#X text 496 497 VISUALIZATIONS:;
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+#X text 323 579 <- Audio IO;
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+#X text 69 581 <- Visualization IO;
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+#X text 83 612 <- temporal lowpass for spectrum view (0...100);
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+#X text 229 532 learning rate (mu):;
+#X text 26 26 ADAPTIVE EQUALIZATION: INVERSE MODELING;
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+#X text 532 551 (2) H_chan(z) (= channel);
+#X text 516 572 using mode:;
+#X text 529 589 (1) H_eq(z) (= 1/H_chan(z) = equalizer);
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