diff --git a/eband_train.py b/eband_train.py
deleted file mode 100644
index 086668d..0000000
--- a/eband_train.py
+++ /dev/null
@@ -1,208 +0,0 @@
-#!/usr/bin/python3
-# eband_train.py
-#
-# David Rowe Dec 2019
-#
-# Train a NN to model to transform rate K=14 LPCnetstyle eband vectors
-# to rate L {Am} samples.  See if we can get better speech quality
-# using small dimension vectors that will be easier to quantise.
-
-'''
-  usage: ./src/c2sim ~/Downloads/train_8k.sw --modelout ~/phasenn/train_8k.model --bands ~/phasenn/train_8k.f32
-         ./eband_train.py train_8k.f32 train_8k.model --epochs 10
-'''
-
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-from scipy import signal
-import codec2_model
-import argparse
-import os
-from keras.layers import Input, Dense, Concatenate
-from keras import models,layers
-from keras import initializers
-from keras import backend as K
-
-# less verbose tensorflow ....
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-# constants
-
-width             = 256
-nb_batch          = 32
-newamp1_K         = 20
-max_amp           = 160 
-nb_plots          = 6
-N                 = 80
-
-def list_str(values):
-    return values.split(',')
-
-parser = argparse.ArgumentParser(description='Train a NN to decode Codec 2 rate K -> rate L')
-parser.add_argument('featurefile', help='f32 file of newamp1 rate K vectors')
-parser.add_argument('modelfile', help='Codec 2 model records with rate L vectors')
-parser.add_argument('--frames', type=list_str, default="30,31,32,33,34,35", help='Frames to view')
-parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
-parser.add_argument('--nb_samples', type=int, default=1000000, help='Number of frames to train on')
-args = parser.parse_args()
-assert nb_plots == len(args.frames)
-
-# read in model file records
-Wo, L, A, phase, voiced = codec2_model.read(args.modelfile, args.nb_samples)
-nb_samples = Wo.size;
-nb_voiced = np.count_nonzero(voiced)
-print("nb_samples: %d voiced %d" % (nb_samples, nb_voiced))
-
-# read in rate K vectors
-features = np.fromfile(args.featurefile, dtype='float32')
-nb_features = 1 + newamp1_K + newamp1_K + max_amp
-nb_samples1 = len(features)/nb_features
-print("nb_samples1: %f" % (nb_samples1))
-print( nb_samples == nb_samples1)
-assert nb_samples == nb_samples1
-features = np.reshape(features, (nb_samples, nb_features))
-print(features.shape)
-rateK = features[:,1:1+newamp1_K]
-print(rateK.shape)
-A_conventional = features[:,2*newamp1_K+1:]
-print(A_conventional.shape)
-
-# find and subtract mean for each frame
-mean_amp = np.zeros(nb_samples)
-for i in range(nb_samples):
-    mean_amp[i] = np.mean(np.log10(A[i,1:L[i]+1]))
-
-# set up sparse amp output vectors
-amp_sparse = np.zeros((nb_samples, width))
-for i in range(nb_samples):
-    for m in range(1,L[i]+1):
-        bin = int(np.round(m*Wo[i]*width/np.pi)); bin = min(width-1, bin)
-        amp_sparse[i,bin] = np.log10(A[i,m]) - mean_amp[i]
-
-# our model
-model = models.Sequential()
-model.add(layers.Dense(2*newamp1_K, activation='relu', input_dim=newamp1_K))
-model.add(layers.Dense(2*width, activation='relu'))
-model.add(layers.Dense(width))
-model.summary()
-
-# custom loss function - we only care about outputs at the non-zero
-# positions in the sparse y_true vector.  To avoid driving the other
-# samples to 0 we use a sparse loss function.  The normalisation term
-# accounts for the time varying number of non-zero samples per frame.
-def sparse_loss(y_true, y_pred):
-    mask = K.cast( K.not_equal(y_true, 0), dtype='float32')
-    n = K.sum(mask)
-    return K.sum(K.square((y_pred - y_true)*mask))/n
-
-# testing custom loss function
-y_true = Input(shape=(None,))
-y_pred = Input(shape=(None,))
-loss_func = K.Function([y_true, y_pred], [sparse_loss(y_true, y_pred)])
-assert loss_func([[[0,1,0]], [[2,2,2]]]) == np.array([1])
-assert loss_func([[[1,1,0]], [[3,2,2]]]) == np.array([2.5])
-assert loss_func([[[0,1,0]], [[0,2,0]]]) == np.array([1])
-
-# fit the model
-from keras import optimizers
-sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
-model.compile(loss=sparse_loss, optimizer=sgd)
-history = model.fit(rateK, amp_sparse, batch_size=nb_batch, epochs=args.epochs, validation_split=0.1)
-
-# try model over training database
-amp_sparse_est = model.predict(rateK)
-
-# extract amplitudes from sparse vector and estimate variance of
-# quantisation error (mean error squared between original and
-# quantised magnitudes, the spectral distortion)
-amp_est = np.zeros((nb_samples,width))
-error = np.zeros(nb_samples)
-errorc = np.zeros(nb_samples)
-e1 = 0; n = 0; ec1 = 0
-for i in range(nb_samples):
-    e2 = 0; ec2 = 0
-    for m in range(1,L[i]+1):
-        bin = int(np.round(m*Wo[i]*width/np.pi)); bin = min(width-1, bin)
-        amp_est[i,m] = amp_sparse_est[i,bin]
-        e = (amp_sparse_est[i,bin] - amp_sparse[i,bin]) ** 2
-        n+=1; e1 += e; e2 += e;
-        ec = (np.log10(A_conventional[i,m]) - mean_amp[i] - amp_sparse[i,bin]) ** 2
-        ec1 += ec; ec2 += ec
-    error[i] = e2/L[i]
-    errorc[i] = ec2/L[i]
-# mean of error squared is actually the variance
-print("var1: %3.2f var2: %3.2f varc: %3.2f (dB*dB)" % (100*e1/n,100*np.mean(error),100*ec1/n,))
-      
-# synthesise time domain signal
-def sample_time(r, A):
-    s = np.zeros(2*N);
-    for m in range(1,L[r]+1):
-        s = s + A[m]*np.cos(m*Wo[r]*range(-N,N) + phase[r,m])
-    return s
-
-# plot results
-
-frames = np.array(args.frames,dtype=int)
-nb_plots = frames.size
-nb_plotsy = np.floor(np.sqrt(nb_plots)); nb_plotsx=nb_plots/nb_plotsy;
-
-plt.figure(1)
-plt.plot(history.history['loss'])
-plt.plot(history.history['val_loss'])
-plt.legend(['train', 'valid'], loc='upper right')
-plt.title('model loss')
-plt.xlabel('epoch')
-plt.show(block=False)
-
-plt.figure(2)
-plt.title('Amplitudes Spectra')
-for r in range(nb_plots):
-    plt.subplot(nb_plotsy,nb_plotsx,r+1)
-    f = int(frames[r]/4);
-    plt.plot(np.log10(A[f,1:L[f]])-mean_amp[f],'g')
-    plt.plot(0+amp_est[f,1:L[f]],'r')
-    plt.plot(0+np.log10(A_conventional[f,1:L[f]])-mean_amp[f],'b')
-    t = "frame %d" % (f)
-    plt.title(t)
-    print(error[f],errorc[f])
-plt.show(block=False)
-
-plt.figure(3)
-plt.title('Time Domain')
-for r in range(nb_plots):
-    plt.subplot(nb_plotsy,nb_plotsx,r+1)
-    f = int(frames[r]/4);
-    s = sample_time(f, A[f,:])
-    A_est = 10**(amp_est[f,:] + mean_amp[f])
-    s_est = sample_time(f, A_est)
-    plt.plot(range(-N,N),s,'g')
-    plt.plot(range(-N,N),s_est,'r') 
-plt.show(block=False)
-
-plt.figure(4)
-plt.title('Histogram of mean error squared per frame')
-plt.subplot(211)
-plt.hist(error,20, range=(0,0.15))
-plt.subplot(212)
-plt.hist(errorc,20, range=(0,0.15))
-plt.show(block=False)
-
-plt.figure(5)
-plt.title('error squared against frame energy')
-plt.subplot(211)
-plt.scatter(mean_amp, error)
-plt.subplot(212)
-plt.scatter(mean_amp, errorc)
-plt.show(block=False)
-
-plt.figure(6)
-plt.subplot(211)
-plt.plot(error[:300])
-plt.subplot(212)
-plt.plot(errorc[:300])
-plt.show(block=False)
-
-print("Click on last figure to finish....")
-plt.waitforbuttonpress(0)
-plt.close()
diff --git a/newamp1_train.py b/newamp1_train.py
deleted file mode 100755
index 82c5f31..0000000
--- a/newamp1_train.py
+++ /dev/null
@@ -1,207 +0,0 @@
-#!/usr/bin/python3
-# newamp1_train.py
-#
-# David Rowe Dec 2019
-#
-# Train a NN to model to transform newamp1 rate K vectors to rate L
-# {Am} samples.  See if we can get better speech quality than regular
-# DSP algorithms.  Effectively an alternate Codec 2 700C decoder
-'''
-  usage: ./src/c2enc 700C ~/Downloads/train_8k.sw /dev/null --mlfeat ~/phasenn/train_8k.f32 ~/phasenn/train_8k.model --eq
-         ./newamp1_train.py train_8k.f32 train_8k.model --epochs 10
-'''
-
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-from scipy import signal
-import codec2_model
-import argparse
-import os
-from keras.layers import Input, Dense, Concatenate
-from keras import models,layers
-from keras import initializers
-from keras import backend as K
-
-# less verbose tensorflow ....
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-# constants
-
-width             = 256
-nb_batch          = 32
-newamp1_K         = 20
-max_amp           = 160 
-nb_plots          = 6
-N                 = 80
-
-def list_str(values):
-    return values.split(',')
-
-parser = argparse.ArgumentParser(description='Train a NN to decode Codec 2 rate K -> rate L')
-parser.add_argument('featurefile', help='f32 file of newamp1 rate K vectors')
-parser.add_argument('modelfile', help='Codec 2 model records with rate L vectors')
-parser.add_argument('--frames', type=list_str, default="30,31,32,33,34,35", help='Frames to view')
-parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
-parser.add_argument('--nb_samples', type=int, default=1000000, help='Number of frames to train on')
-args = parser.parse_args()
-assert nb_plots == len(args.frames)
-
-# read in model file records
-Wo, L, A, phase, voiced = codec2_model.read(args.modelfile, args.nb_samples)
-nb_samples = Wo.size;
-nb_voiced = np.count_nonzero(voiced)
-print("nb_samples: %d voiced %d" % (nb_samples, nb_voiced))
-
-# read in rate K vectors
-features = np.fromfile(args.featurefile, dtype='float32')
-nb_features = 1 + newamp1_K + newamp1_K + max_amp
-nb_samples1 = len(features)/nb_features
-print("nb_samples1: %f" % (nb_samples1))
-print( nb_samples == nb_samples1)
-assert nb_samples == nb_samples1
-features = np.reshape(features, (nb_samples, nb_features))
-print(features.shape)
-rateK = features[:,1:1+newamp1_K]
-print(rateK.shape)
-A_conventional = features[:,2*newamp1_K+1:]
-print(A_conventional.shape)
-
-# find and subtract mean for each frame
-mean_amp = np.zeros(nb_samples)
-for i in range(nb_samples):
-    mean_amp[i] = np.mean(np.log10(A[i,1:L[i]+1]))
-
-# set up sparse amp output vectors
-amp_sparse = np.zeros((nb_samples, width))
-for i in range(nb_samples):
-    for m in range(1,L[i]+1):
-        bin = int(np.round(m*Wo[i]*width/np.pi)); bin = min(width-1, bin)
-        amp_sparse[i,bin] = np.log10(A[i,m]) - mean_amp[i]
-
-# our model
-model = models.Sequential()
-model.add(layers.Dense(2*newamp1_K, activation='relu', input_dim=newamp1_K))
-model.add(layers.Dense(2*width, activation='relu'))
-model.add(layers.Dense(width))
-model.summary()
-
-# custom loss function - we only care about outputs at the non-zero
-# positions in the sparse y_true vector.  To avoid driving the other
-# samples to 0 we use a sparse loss function.  The normalisation term
-# accounts for the time varying number of non-zero samples per frame.
-def sparse_loss(y_true, y_pred):
-    mask = K.cast( K.not_equal(y_true, 0), dtype='float32')
-    n = K.sum(mask)
-    return K.sum(K.square((y_pred - y_true)*mask))/n
-
-# testing custom loss function
-y_true = Input(shape=(None,))
-y_pred = Input(shape=(None,))
-loss_func = K.Function([y_true, y_pred], [sparse_loss(y_true, y_pred)])
-assert loss_func([[[0,1,0]], [[2,2,2]]]) == np.array([1])
-assert loss_func([[[1,1,0]], [[3,2,2]]]) == np.array([2.5])
-assert loss_func([[[0,1,0]], [[0,2,0]]]) == np.array([1])
-
-# fit the model
-from keras import optimizers
-sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
-model.compile(loss=sparse_loss, optimizer=sgd)
-history = model.fit(rateK, amp_sparse, batch_size=nb_batch, epochs=args.epochs, validation_split=0.1)
-
-# try model over training database
-amp_sparse_est = model.predict(rateK)
-
-# extract amplitudes from sparse vector and estimate variance of
-# quantisation error (mean error squared between original and
-# quantised magnitudes, the spectral distortion)
-amp_est = np.zeros((nb_samples,width))
-error = np.zeros(nb_samples)
-errorc = np.zeros(nb_samples)
-e1 = 0; n = 0; ec1 = 0
-for i in range(nb_samples):
-    e2 = 0; ec2 = 0
-    for m in range(1,L[i]+1):
-        bin = int(np.round(m*Wo[i]*width/np.pi)); bin = min(width-1, bin)
-        amp_est[i,m] = amp_sparse_est[i,bin]
-        e = (amp_sparse_est[i,bin] - amp_sparse[i,bin]) ** 2
-        n+=1; e1 += e; e2 += e;
-        ec = (np.log10(A_conventional[i,m]) - mean_amp[i] - amp_sparse[i,bin]) ** 2
-        ec1 += ec; ec2 += ec
-    error[i] = e2/L[i]
-    errorc[i] = ec2/L[i]
-# mean of error squared is actually the variance
-print("var1: %3.2f var2: %3.2f varc: %3.2f (dB*dB)" % (100*e1/n,100*np.mean(error),100*ec1/n,))
-      
-# synthesise time domain signal
-def sample_time(r, A):
-    s = np.zeros(2*N);
-    for m in range(1,L[r]+1):
-        s = s + A[m]*np.cos(m*Wo[r]*range(-N,N) + phase[r,m])
-    return s
-
-# plot results
-
-frames = np.array(args.frames,dtype=int)
-nb_plots = frames.size
-nb_plotsy = np.floor(np.sqrt(nb_plots)); nb_plotsx=nb_plots/nb_plotsy;
-
-plt.figure(1)
-plt.plot(history.history['loss'])
-plt.plot(history.history['val_loss'])
-plt.legend(['train', 'valid'], loc='upper right')
-plt.title('model loss')
-plt.xlabel('epoch')
-plt.show(block=False)
-
-plt.figure(2)
-plt.title('Amplitudes Spectra')
-for r in range(nb_plots):
-    plt.subplot(nb_plotsy,nb_plotsx,r+1)
-    f = int(frames[r]/4);
-    plt.plot(np.log10(A[f,1:L[f]])-mean_amp[f],'g')
-    plt.plot(0+amp_est[f,1:L[f]],'r')
-    plt.plot(0+np.log10(A_conventional[f,1:L[f]])-mean_amp[f],'b')
-    t = "frame %d" % (f)
-    plt.title(t)
-    print(error[f],errorc[f])
-plt.show(block=False)
-
-plt.figure(3)
-plt.title('Time Domain')
-for r in range(nb_plots):
-    plt.subplot(nb_plotsy,nb_plotsx,r+1)
-    f = int(frames[r]/4);
-    s = sample_time(f, A[f,:])
-    A_est = 10**(amp_est[f,:] + mean_amp[f])
-    s_est = sample_time(f, A_est)
-    plt.plot(range(-N,N),s,'g')
-    plt.plot(range(-N,N),s_est,'r') 
-plt.show(block=False)
-
-plt.figure(4)
-plt.title('Histogram of mean error squared per frame')
-plt.subplot(211)
-plt.hist(error,20, range=(0,0.15))
-plt.subplot(212)
-plt.hist(errorc,20, range=(0,0.15))
-plt.show(block=False)
-
-plt.figure(5)
-plt.title('error squared against frame energy')
-plt.subplot(211)
-plt.scatter(mean_amp, error)
-plt.subplot(212)
-plt.scatter(mean_amp, errorc)
-plt.show(block=False)
-
-plt.figure(6)
-plt.subplot(211)
-plt.plot(error[:300])
-plt.subplot(212)
-plt.plot(errorc[:300])
-plt.show(block=False)
-
-print("Click on last figure to finish....")
-plt.waitforbuttonpress(0)
-plt.close()
diff --git a/rateK_train.py b/rateK_train.py
deleted file mode 100755
index 170aa64..0000000
--- a/rateK_train.py
+++ /dev/null
@@ -1,159 +0,0 @@
-#!/usr/bin/python3
-# rateK_train.py
-#
-# David Rowe Dec 2019
-#
-# Experiments in interpolating rate K vectors using NN's and other
-# techniques.
-
-'''
-  Usage:
-
-  $ c2sim ~/Downloads/train_8k.sw --rateK --rateKout ~/phasenn/rateK.f32
-  $ ./rateK_train.py rateK.f32 --dec 4 --frame 30 --epochs 25
-'''
-
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-from scipy import signal
-import codec2_model
-import argparse
-import os
-from keras.layers import Input, Dense, Concatenate
-from keras import models,layers
-from keras import initializers
-from keras import backend as K
-
-# less verbose tensorflow ....
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-# constants
-
-nb_batch          = 32
-newamp1_K         = 20
-nb_plots          = 6
-N                 = 80
-
-def list_str(values):
-    return values.split(',')
-
-parser = argparse.ArgumentParser(description='Train a NN to interpolate rate K vectors')
-parser.add_argument('featurefile', help='f32 file of newamp1 rate K vectors')
-parser.add_argument('--dec', type=int, default=4, help='decimation rate')
-parser.add_argument('--frame', type=int, default="30", help='Frames to view')
-parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
-args = parser.parse_args()
-dec = args.dec
-
-# read in rate K vectors
-features = np.fromfile(args.featurefile, dtype='float32')
-nb_features = newamp1_K
-nb_samples = int(len(features)/nb_features)
-print("nb_samples: %d" % (nb_samples))
-rateK = np.reshape(features, (nb_samples, nb_features))/20
-print(rateK.shape)
-
-# set up training data
-nb_vecs = int(nb_samples/dec)
-inputs  = np.zeros((nb_vecs, 2*newamp1_K))
-outputs = np.zeros((nb_vecs, 3*newamp1_K))
-outputs_lin = np.zeros((nb_vecs, 3*newamp1_K))
-outputs_linpf = np.zeros((nb_vecs, 3*newamp1_K))
-nv = 0
-for i in range(0,nb_samples-dec,dec):
-    inputs[nv,:newamp1_K] = rateK[i,:]
-    inputs[nv,newamp1_K:] = rateK[i+dec,:]
-    for j in range(dec-1):
-        st = j*newamp1_K
-        outputs[nv,st:st+newamp1_K] = rateK[i+1+j,:]
-    # linear interpolation for reference
-    c = 1.0/dec; inc = 1.0/dec;
-    for j in range(dec-1):
-        st = j*newamp1_K
-        outputs_lin[nv,st:st+newamp1_K] = (1-c)*inputs[nv,:newamp1_K] + c*inputs[nv,newamp1_K:]
-        c += inc
-    # linear interpolation with per frame selection of c
-    for j in range(dec-1):
-        A = inputs[nv,:newamp1_K]; B = inputs[nv,newamp1_K:];
-        T = rateK[i+1+j,:]
-        c = -np.dot((B-T),(A-B))/np.dot((A-B),(A-B))
-        st = j*newamp1_K
-        outputs_linpf[nv,st:st+newamp1_K] = c*A + (1-c)*B
-    
-    nv += 1
-print(inputs.shape, outputs.shape)
-
-nn = 1
-if nn:
-    # our model
-    model = models.Sequential()
-    model.add(layers.Dense(3*newamp1_K, activation='tanh', input_dim=2*newamp1_K))
-    model.add(layers.Dense(3*newamp1_K, activation='tanh'))
-    model.add(layers.Dense(3*newamp1_K))
-    model.summary()
-
-    # fit the model
-    from keras import optimizers
-    #sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
-    model.compile(loss='mse', optimizer="adam")
-    history = model.fit(inputs, outputs, batch_size=nb_batch, epochs=args.epochs, validation_split=0.1)
-
-    # test the model on the training data
-    outputs_nnest = model.predict(inputs)
-
-    plt.figure(1)
-    plt.plot(history.history['loss'])
-    plt.plot(history.history['val_loss'])
-    plt.legend(['train', 'valid'], loc='upper right')
-    plt.title('model loss')
-    plt.xlabel('epoch')
-    plt.show(block=False)
-
-# plot results over all frames
-var_lin = np.var(20*outputs-20*outputs_lin)
-var_linpf = np.var(20*outputs-20*outputs_linpf)
-var_nnest = np.var(20*outputs-20*outputs_nnest)
-print("var_lin: %3.2f var_linpf: %3.2f var_nnest: %3.2f" % (var_lin, var_linpf, var_nnest))
-
-# plot results for a few frames
-
-nb_plots = dec+1; nb_plotsy = 1; nb_plotsx = nb_plots
-frame = int(args.frame/dec)
-
-plt.figure(2)
-
-loop = True
-print("Press key to advance, mouse click on last figure to finish....")
-while loop:
-    plt.title('rate K Amplitude Spectra')
-    for d in range(dec+1):
-        plt.subplot(1, nb_plots, d+1)
-        if d == 0:
-            plt.plot(inputs[frame,:newamp1_K],'g')
-        elif d == dec:
-            plt.plot(inputs[frame,newamp1_K:],'g')
-        else: 
-            st = (d-1)*newamp1_K
-            plt.plot(outputs[frame,st:st+newamp1_K],'g')
-            plt.plot(outputs_lin[frame,st:st+newamp1_K],'b')
-            if nn:
-                plt.plot(outputs_nnest[frame,st:st+newamp1_K],'r')
-            else:
-                plt.plot(outputs_linpf[frame,st:st+newamp1_K],'r')
-        plt.ylim((-1,4))
-    var_lin = np.var(20*outputs[frame,:]-20*outputs_lin[frame,:])
-    var_linpf = np.var(20*outputs[frame,:]-20*outputs_linpf[frame,:])
-    print("frame: %d var_lin: %3.2f " % (frame,var_lin), end='')
-    if nn:
-        var_nnest = np.var(20*outputs[frame,:]-20*outputs_nnest[frame,:])
-        print("var_nnest: %3.2f" % (var_nnest), end='')
-    else:
-        print("var_linpf: %3.2f" % (var_linpf), end='')
-        
-    print(flush=True)
-    plt.show(block=False)
-
-    loop = plt.waitforbuttonpress(0)
-    frame += 1
-    plt.clf()