Output training messages to stderr instead of stdout.

[c64tapwav] / decode.cpp

diff --git a/decode.cpp b/decode.cpp
index d1b291f025d711b62f47d8e5d233311e3b29f071..f645588923bcb079f6f30b52f505f07535ab71f8 100644 (file)
--- a/decode.cpp
+++ b/decode.cpp
@@ -340,11 +340,23 @@ std::vector<float> do_fir_filter(const std::vector<float>& pcm, const float* fil
 
 std::vector<float> do_rc_filter(const std::vector<float>& pcm, float freq, int sample_rate)
 {
+       // This is only a 6 dB/oct filter, which seemingly works better
+       // than the Filter class, which is a standard biquad (12 dB/oct).
+       // The b/c calculations come from libnyquist (atone.c);
+       // I haven't checked, but I suppose they fall out of the bilinear
+       // transform of the transfer function H(s) = s/(s + w).
        std::vector<float> filtered_pcm;
        filtered_pcm.resize(pcm.size());
-       Filter filter = Filter::hpf(M_PI * freq / sample_rate);
+       const float b = 2.0f - cos(2.0 * M_PI * freq / sample_rate);
+       const float c = b - sqrt(b * b - 1.0f);
+       float prev_in = 0.0f;
+       float prev_out = 0.0f;
        for (unsigned i = 0; i < pcm.size(); ++i) {
-               filtered_pcm[i] = filter.update(pcm[i]);
+               float in = pcm[i];
+               float out = c * (prev_out + in - prev_in);
+               filtered_pcm[i] = out;
+               prev_in = in;
+               prev_out = out;
        }
 
        if (output_filtered) {
@@ -465,7 +477,7 @@ void find_kmeans(const std::vector<pulse> &pulses, double calibration_factor, co
                bool any_moved = false;
                for (unsigned i = 0; i < initial_centers.size(); ++i) {
                        if (num[i] == 0) {
-                               printf("K-means broke down, can't output new reference training points\n");
+                               fprintf(stderr, "K-means broke down, can't output new reference training points\n");
                                return;
                        }
                        float new_center = sums[i] / num[i];
@@ -478,11 +490,11 @@ void find_kmeans(const std::vector<pulse> &pulses, double calibration_factor, co
                        break;
                }
        }
-       printf("New reference training points:");
+       fprintf(stderr, "New reference training points:");
        for (unsigned i = 0; i < last_centers.size(); ++i) {
-               printf(" %.3f", last_centers[i]);
+               fprintf(stderr, " %.3f", last_centers[i]);
        }
-       printf("\n");
+       fprintf(stderr, "\n");
 }
 
 void spsa_train(const std::vector<float> &pcm, int sample_rate)