diff --git a/dnn/ceps_vq_train.c b/dnn/ceps_vq_train.c
new file mode 100644
index 00000000..8a3b5e47
--- /dev/null
+++ b/dnn/ceps_vq_train.c
@@ -0,0 +1,399 @@
+
+#include <valgrind/memcheck.h>
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <math.h>
+
+#define MIN(a,b) ((a)<(b)?(a):(b))
+#define COEF 0.75f
+#define MAX_ENTRIES 16384
+
+void compute_weights(const float *x, float *w, int ndim)
+{
+  int i;
+  w[0] = MIN(x[0], x[1]-x[0]);
+  for (i=1;i<ndim-1;i++)
+    w[i] = MIN(x[i]-x[i-1], x[i+1]-x[i]);
+  w[ndim-1] = MIN(x[ndim-1]-x[ndim-2], M_PI-x[ndim-1]);
+  
+  for (i=0;i<ndim;i++)
+    w[i] = 1./(.01+w[i]);
+  w[0]*=3;
+  w[1]*=2;
+  /*
+  for (i=0;i<ndim;i++)
+    w[i] = 1;*/
+}
+
+int find_nearest(const float *codebook, int nb_entries, const float *x, int ndim, float *dist)
+{
+  int i, j;
+  float min_dist = 1e15;
+  int nearest = 0;
+  
+  for (i=0;i<nb_entries;i++)
+  {
+    float dist=0;
+    for (j=0;j<ndim;j++)
+      dist += (x[j]-codebook[i*ndim+j])*(x[j]-codebook[i*ndim+j]);
+    if (dist<min_dist)
+    {
+      min_dist = dist;
+      nearest = i;
+    }
+  }
+  if (dist)
+    *dist = min_dist;
+  return nearest;
+}
+
+int find_nearest_weighted(const float *codebook, int nb_entries, float *x, const float *w, int ndim)
+{
+  int i, j;
+  float min_dist = 1e15;
+  int nearest = 0;
+  
+  for (i=0;i<nb_entries;i++)
+  {
+    float dist=0;
+    for (j=0;j<ndim;j++)
+      dist += w[j]*(x[j]-codebook[i*ndim+j])*(x[j]-codebook[i*ndim+j]);
+    if (dist<min_dist)
+    {
+      min_dist = dist;
+      nearest = i;
+    }
+  }
+  return nearest;
+}
+
+int quantize_lsp(const float *x, const float *codebook1, const float *codebook2, 
+		 const float *codebook3, int nb_entries, float *xq, int ndim)
+{
+  int i, n1, n2, n3;
+  float err[ndim], err2[ndim], err3[ndim];
+  float w[ndim], w2[ndim], w3[ndim];
+  
+  w[0] = MIN(x[0], x[1]-x[0]);
+  for (i=1;i<ndim-1;i++)
+    w[i] = MIN(x[i]-x[i-1], x[i+1]-x[i]);
+  w[ndim-1] = MIN(x[ndim-1]-x[ndim-2], M_PI-x[ndim-1]);
+  
+  /*
+  for (i=0;i<ndim;i++)
+    w[i] = 1./(.003+w[i]);
+  w[0]*=3;
+  w[1]*=2;*/
+  compute_weights(x, w, ndim);
+  
+  for (i=0;i<ndim;i++)
+    err[i] = x[i]-COEF*xq[i];
+  n1 = find_nearest(codebook1, nb_entries, err, ndim, NULL);
+  
+  for (i=0;i<ndim;i++)
+  {
+    xq[i] = COEF*xq[i] + codebook1[ndim*n1+i];
+    err[i] -= codebook1[ndim*n1+i];
+  }
+  for (i=0;i<ndim/2;i++)
+  {
+    err2[i] = err[2*i];  
+    err3[i] = err[2*i+1];
+    w2[i] = w[2*i];  
+    w3[i] = w[2*i+1];
+  }
+  n2 = find_nearest_weighted(codebook2, nb_entries, err2, w2, ndim/2);
+  n3 = find_nearest_weighted(codebook3, nb_entries, err3, w3, ndim/2);
+  
+  for (i=0;i<ndim/2;i++)
+  {
+    xq[2*i] += codebook2[ndim*n2/2+i];
+    xq[2*i+1] += codebook3[ndim*n3/2+i];
+  }
+  return 0;
+}
+
+void split(float *codebook, int nb_entries, int ndim)
+{
+  int i,j;
+  for (i=0;i<nb_entries;i++)
+  {
+    for (j=0;j<ndim;j++)
+    {
+      float delta = .01*(rand()/(float)RAND_MAX-.5);
+      codebook[i*ndim+j] += delta;
+      codebook[(i+nb_entries)*ndim+j] = codebook[i*ndim+j] - delta;
+    }
+  }
+}
+
+
+void split1(float *codebook, int nb_entries, const float *data, int nb_vectors, int ndim)
+{
+  int i,j;
+  int nearest[nb_vectors];
+  float dist[nb_entries];
+  int count[nb_entries];
+  int worst;
+  for (i=0;i<nb_entries;i++)
+    dist[i] = 0;
+  for (i=0;i<nb_entries;i++)
+    count[i]=0;
+  for (i=0;i<nb_vectors;i++)
+  {
+    float d;
+    nearest[i] = find_nearest(codebook, nb_entries, data+i*ndim, ndim, &d);
+    dist[nearest[i]] += d;
+    count[nearest[i]]++;
+  }
+
+  worst=0;
+  for (i=1;i<nb_entries;i++)
+  {
+    if (dist[i] > dist[worst])
+      worst=i;
+  }
+  
+  for (j=0;j<ndim;j++)
+  {
+    float delta = .001*(rand()/(float)RAND_MAX-.5);
+    codebook[worst*ndim+j] += delta;
+    codebook[nb_entries*ndim+j] = codebook[worst*ndim+j] - delta;
+  }
+}
+
+
+
+void update(float *data, int nb_vectors, float *codebook, int nb_entries, int ndim)
+{
+  int i,j;
+  int count[nb_entries];
+  int nearest[nb_vectors];
+  double err=0;
+
+  for (i=0;i<nb_entries;i++)
+    count[i] = 0;
+  
+  for (i=0;i<nb_vectors;i++)
+  {
+    float dist;
+    nearest[i] = find_nearest(codebook, nb_entries, data+i*ndim, ndim, &dist);
+    err += dist;
+  }
+  printf("RMS error = %f\n", sqrt(err/nb_vectors/ndim));
+  for (i=0;i<nb_entries*ndim;i++)
+    codebook[i] = 0;
+  
+  for (i=0;i<nb_vectors;i++)
+  {
+    int n = nearest[i];
+    count[n]++;
+    for (j=0;j<ndim;j++)
+      codebook[n*ndim+j] += data[i*ndim+j];
+  }
+
+  float w2=0;
+  for (i=0;i<nb_entries;i++)
+  { 
+    for (j=0;j<ndim;j++)
+      codebook[i*ndim+j] *= (1./count[i]);
+    w2 += (count[i]/(float)nb_vectors)*(count[i]/(float)nb_vectors);
+  }
+  //fprintf(stderr, "%f / %d\n", 1./w2, nb_entries);
+}
+
+void update_weighted(float *data, float *weight, int nb_vectors, float *codebook, int nb_entries, int ndim)
+{
+  int i,j;
+  float count[MAX_ENTRIES][ndim];
+  int nearest[nb_vectors];
+  
+  for (i=0;i<nb_entries;i++)
+    for (j=0;j<ndim;j++)
+      count[i][j] = 0;
+  
+  for (i=0;i<nb_vectors;i++)
+  {
+    nearest[i] = find_nearest_weighted(codebook, nb_entries, data+i*ndim, weight+i*ndim, ndim);
+  }
+  for (i=0;i<nb_entries*ndim;i++)
+    codebook[i] = 0;
+  
+  for (i=0;i<nb_vectors;i++)
+  {
+    int n = nearest[i];
+    for (j=0;j<ndim;j++)
+    {
+      float w = sqrt(weight[i*ndim+j]);
+      count[n][j]+=w;
+      codebook[n*ndim+j] += w*data[i*ndim+j];
+    }
+  }
+
+  //float w2=0;
+  for (i=0;i<nb_entries;i++)
+  { 
+    for (j=0;j<ndim;j++)
+      codebook[i*ndim+j] *= (1./count[i][j]);
+    //w2 += (count[i]/(float)nb_vectors)*(count[i]/(float)nb_vectors);
+  }
+  //fprintf(stderr, "%f / %d\n", 1./w2, nb_entries);
+}
+
+void vq_train(float *data, int nb_vectors, float *codebook, int nb_entries, int ndim)
+{
+  int i, j, e;
+  e = 1;
+  for (j=0;j<ndim;j++)
+    codebook[j] = 0;
+  for (i=0;i<nb_vectors;i++)
+    for (j=0;j<ndim;j++)
+      codebook[j] += data[i*ndim+j];
+  for (j=0;j<ndim;j++)
+    codebook[j] *= (1./nb_vectors);
+  
+  
+  while (e< nb_entries)
+  {
+#if 1
+    split(codebook, e, ndim);
+    e<<=1;
+#else
+    split1(codebook, e, data, nb_vectors, ndim);
+    e++;
+#endif
+    fprintf(stderr, "%d\n", e);
+    for (j=0;j<4;j++)
+      update(data, nb_vectors, codebook, e, ndim);
+  }
+  for (j=0;j<ndim*2;j++)
+    update(data, nb_vectors, codebook, e, ndim);
+}
+
+void vq_train_weighted(float *data, float *weight, int nb_vectors, float *codebook, int nb_entries, int ndim)
+{
+  int i, j, e;
+  e = 1;
+  for (j=0;j<ndim;j++)
+    codebook[j] = 0;
+  for (i=0;i<nb_vectors;i++)
+    for (j=0;j<ndim;j++)
+      codebook[j] += data[i*ndim+j];
+  for (j=0;j<ndim;j++)
+    codebook[j] *= (1./nb_vectors);
+  
+  
+  while (e< nb_entries)
+  {
+#if 0
+    split(codebook, e, ndim);
+    e<<=1;
+#else
+    split1(codebook, e, data, nb_vectors, ndim);
+    e++;
+#endif
+    fprintf(stderr, "%d\n", e);
+    for (j=0;j<ndim;j++)
+      update_weighted(data, weight, nb_vectors, codebook, e, ndim);
+  }
+}
+
+
+int main(int argc, char **argv)
+{
+  int i,j;
+  int nb_vectors, nb_entries, ndim, ndim0, total_dim;
+  float *data, *pred, *codebook, *codebook2;
+  float *delta;
+  double err;
+  FILE *fout;
+  
+  ndim = atoi(argv[1]);
+  ndim0 = ndim-1;
+  total_dim = atoi(argv[2]);
+  nb_vectors = atoi(argv[3]);
+  nb_entries = 1<<atoi(argv[4]);
+  
+  data = malloc((nb_vectors*ndim+total_dim)*sizeof(*data));
+  pred = malloc(nb_vectors*ndim0*sizeof(*pred));
+  codebook = malloc(nb_entries*ndim0*sizeof(*codebook));
+  codebook2 = malloc(nb_entries*ndim0*sizeof(*codebook2));
+  
+  for (i=0;i<nb_vectors;i++)
+  {
+    fread(&data[i*ndim], sizeof(float), total_dim, stdin);
+    if (feof(stdin))
+      break;
+  }
+  nb_vectors = i;
+  VALGRIND_CHECK_MEM_IS_DEFINED(data, nb_entries*ndim);
+
+  for (i=0;i<4;i++)
+  {
+    for (j=0;j<ndim0;j++)
+      pred[i*ndim0+j] = 0;
+  }
+  for (i=4;i<nb_vectors;i++)
+  {
+    for (j=0;j<ndim0;j++)
+      pred[i*ndim0+j] = data[i*ndim+j+1] - COEF*data[(i-4)*ndim+j+1];
+  }
+
+  VALGRIND_CHECK_MEM_IS_DEFINED(pred, nb_entries*ndim0);
+  vq_train(pred, nb_vectors, codebook, nb_entries, ndim0);
+  
+  delta = malloc(nb_vectors*ndim0*sizeof(*data));
+  err = 0;
+  for (i=0;i<nb_vectors;i++)
+  {
+    int nearest = find_nearest(codebook, nb_entries, &pred[i*ndim0], ndim0, NULL);
+    for (j=0;j<ndim0;j++)
+    {
+      delta[i*ndim0+j] = pred[i*ndim0+j] - codebook[nearest*ndim0+j];
+      err += delta[i*ndim0+j]*delta[i*ndim0+j];
+    }
+    //printf("\n");
+  }
+  fprintf(stderr, "Cepstrum RMS error: %f\n", sqrt(err/nb_vectors/ndim));
+
+  vq_train(delta, nb_vectors, codebook2, nb_entries, ndim0);
+  
+  err=0;
+  for (i=0;i<nb_vectors;i++)
+  {
+    int n1;
+    n1 = find_nearest(codebook2, nb_entries, &delta[i*ndim0], ndim0, NULL);
+    for (j=0;j<ndim0;j++)
+    {
+      delta[i*ndim0+j] = delta[i*ndim0+j] - codebook2[n1*ndim0+j];
+      err += delta[i*ndim0+j]*delta[i*ndim0+j];
+    }
+  }
+  fprintf(stderr, "Cepstrum RMS error after stage 2: %f)\n", sqrt(err/nb_vectors/ndim));
+  
+  fout = fopen("ceps_codebooks.c", "w");
+  fprintf(fout, "/* This file is automatically generated */\n\n");
+  fprintf(fout, "float ceps_codebook1[%d*%d] = {\n",nb_entries, ndim0);
+  
+  for (i=0;i<nb_entries;i++)
+  {
+    for (j=0;j<ndim0;j++)
+      fprintf(fout, "%f, ", codebook[i*ndim0+j]);
+    fprintf(fout, "\n");
+  }
+  fprintf(fout, "};\n\n");
+
+  fprintf(fout, "float ceps_codebook2[%d*%d] = {\n",nb_entries, ndim0);
+  
+  for (i=0;i<nb_entries;i++)
+  {
+    for (j=0;j<ndim0;j++)
+      fprintf(fout, "%f, ", codebook2[i*ndim0+j]);
+    fprintf(fout, "\n");
+  }
+  fprintf(fout, "};\n\n");
+  
+  fclose(fout);
+  return 0;
+}
diff --git a/dnn/dump_data.c b/dnn/dump_data.c
index b994058e..468c7b69 100644
--- a/dnn/dump_data.c
+++ b/dnn/dump_data.c
@@ -51,6 +51,66 @@
 
 #define NB_FEATURES (2*NB_BANDS+3+LPC_ORDER)
 
+#include "ceps_codebooks.c"
+
+int vq_quantize(const float *codebook, int nb_entries, const float *x, int ndim, float *dist)
+{
+  int i, j;
+  float min_dist = 1e15;
+  int nearest = 0;
+  
+  for (i=0;i<nb_entries;i++)
+  {
+    float dist=0;
+    for (j=0;j<ndim;j++)
+      dist += (x[j]-codebook[i*ndim+j])*(x[j]-codebook[i*ndim+j]);
+    if (dist<min_dist)
+    {
+      min_dist = dist;
+      nearest = i;
+    }
+  }
+  if (dist)
+    *dist = min_dist;
+  return nearest;
+}
+
+#define NB_BANDS_1 (NB_BANDS - 1)
+float vq_mem[NB_BANDS_1];
+int quantize(float *x, float *mem)
+{
+    int i;
+    int id, id2;
+    float ref[NB_BANDS_1];
+    RNN_COPY(ref, x, NB_BANDS_1);
+    for (i=0;i<NB_BANDS_1;i++) {
+        x[i] -= 0.0f*mem[i];
+    }
+    id = vq_quantize(ceps_codebook1, 1024, x, NB_BANDS_1, NULL);
+    for (i=0;i<NB_BANDS_1;i++) {
+        x[i] -= ceps_codebook1[id*NB_BANDS_1 + i];
+    }
+    id2 = vq_quantize(ceps_codebook2, 1024, x, NB_BANDS_1, NULL);
+    for (i=0;i<NB_BANDS_1;i++) {
+        x[i] = ceps_codebook2[id2*NB_BANDS_1 + i];
+    }
+    for (i=0;i<NB_BANDS_1;i++) {
+        x[i] += ceps_codebook1[id*NB_BANDS_1 + i];
+    }
+    for (i=0;i<NB_BANDS_1;i++) {
+        x[i] += 0.0f*mem[i];
+        mem[i] = x[i];
+    }
+    if (0) {
+        float err = 0;
+        for (i=0;i<NB_BANDS_1;i++) {
+            err += (x[i]-ref[i])*(x[i]-ref[i]);
+        }
+        printf("%f\n", sqrt(err/NB_BANDS_1));
+    }
+    
+    return id;
+}
 
 typedef struct {
   float analysis_mem[OVERLAP_SIZE];
@@ -140,6 +200,7 @@ static void compute_frame_features(DenoiseState *st, const float *in) {
     E += Ex[i];
   }
   dct(st->features[st->pcount], Ly);
+  quantize(&st->features[st->pcount][1], vq_mem);
   st->features[st->pcount][0] -= 4;
   g = lpc_from_cepstrum(st->lpc, st->features[st->pcount]);
   st->features[st->pcount][2*NB_BANDS+2] = log10(g);
diff --git a/dnn/lpcnet.py b/dnn/lpcnet.py
index 010f478b..f7a98235 100644
--- a/dnn/lpcnet.py
+++ b/dnn/lpcnet.py
@@ -153,6 +153,11 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, train
     gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)]))
     ulaw_prob = md(gru_out2)
     
+    rnn.trainable=False
+    rnn2.trainable=False
+    md.trainable=False
+    embed.Trainable=False
+    
     model = Model([pcm, feat, pitch], ulaw_prob)
     model.rnn_units1 = rnn_units1
     model.rnn_units2 = rnn_units2
diff --git a/dnn/train_lpcnet.py b/dnn/train_lpcnet.py
index b438f367..d15c4ba0 100755
--- a/dnn/train_lpcnet.py
+++ b/dnn/train_lpcnet.py
@@ -103,8 +103,8 @@ del pred
 del in_exc
 
 # dump models to disk as we go
-checkpoint = ModelCheckpoint('lpcnet24b_384_10_G16_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('lpcnet24fq_384_10_G16_{epoch:02d}.h5')
 
-#model.load_weights('lpcnet9b_384_10_G16_01.h5')
-model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy')
-model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))])
+model.load_weights('lpcnet24f_384_10_G16_31.h5')
+model.compile(optimizer=Adam(0.0005, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy')
+model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))])