jfsantos

from __future__ import print_function

from keras.models import Model, Sequential
from keras.layers import Input, Dense, TimeDistributed
from keras.layers.core import Reshape, Flatten, Dropout, TimeDistributedDense
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Convolution2D
from keras.layers.recurrent import LSTM
from keras.optimizers import Adam

jfsantos

model = nn.Sequential()

lstm = nn.Sequencer(
  nn.Sequential()
  :add(nn.LSTM(nFeatures,nHidden))
  :add(nn.Dropout())
  :add(nn.LSTM(nHidden,nHidden))
)

lstm:remember('neither') -- force model to call forget at each call to forward

jfsantos

signal = require "signal"
complex = require "signal.complex"

function istft(X, win, hop)
    local x = torch.zeros((X:size(1)-1)*hop + win)
    framesamp = X:size(2)
    hopsamp = hop
    for n=1,X:size(1) do
        i = 1 + (n-1)*hopsamp
        print(i, i + framesamp - 1)

jfsantos

creating an lstm with 2 layers
setting forget gate biases to 1 in LSTM layer 1
setting forget gate biases to 1 in LSTM layer 2
number of parameters in the model: 240321
cloning rnn
cloning criterion
/Users/jfsantos/torch/install/bin/luajit: /Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: bad argument #1 to 'set' (expecting number or Tensor or Storage at /tmp/luarocks_torch-scm-1-8315/torch7/generic/Tensor.c:1089)
stack traceback:
	[C]: in function 'set'
	/Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: in function 'func'

jfsantos

fAed cBAG|BBBB gBeB|d2 BD E2 cB|[1cBAF G2 Bc:|[2B2 Bd e3G|
dcde fab2|afef g2 fg|affe fedB|GEDB E3 :|

jfsantos

import numpy as np

x = np.random.rand(513)

# Converting to the frequency domain
X = np.fft.rfft(x)

# Taking the log of the magnitude and converting back to rectangular

def P2R(magnitude, phase):

jfsantos

from collections import defaultdict
import h5py, numpy

class HDF5Matrix:
    refs = defaultdict(int)

    def __init__(self, datapath, dataset, start, end, normalizer=None):
        if datapath not in self.refs.keys():
            f = h5py.File(datapath)
            self.refs[datapath] = f

jfsantos

import numpy as np
import scipy.signal as sig
from adaptfilt import lms

if __name__ == '__main__':
    import matplotlib.pyplot as plt
    from scipy.io import wavfile

    sigma = 0.1
    order = 100

jfsantos

ENV["MOCHA_USE_CUDA"] = "true"

using HDF5, JLD, Mocha

X = Array[]
push!(X, rand(Float32, 128,11*129,1,1))
y = Array[]
push!(y, rand(Float32, 128, 129, 1, 1))

#data_layer = AsyncHDF5DataLayer("train", "train.txt", 128, 1000, [:features, :targets], false, [])

jfsantos

from gammatone.fftweight import fft_gtgram
from scipy.io.matlab import loadmat

s = loadmat("test.mat")["s"][:,0]
fs = 16000

# gt_py has 260 frames
gt_py = fft_gtgram(s, fs, 0.010, 0.0025, 23, 125)

# gt_mat has 269 frames