jfsantos

### Keybase proof

I hereby claim:

  * I am jfsantos on github.
  * I am jfsantos (https://keybase.io/jfsantos) on keybase.
  * I have a public key whose fingerprint is C422 70CC D7E3 C653 09B0  E52E 06AF B67E AD5E 95E5

To claim this, I am signing this object:

jfsantos

using WAV

function inline_audioplayer(filepath)
  markup = """<audio controls="controls" {autoplay}>
              <source src="$filepath" />
              Your browser does not support the audio element.
              </audio>"""

  display(MIME("text/html") ,markup)
end

jfsantos

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import wave
import numpy as np
import scipy as sp

WINSIZE=8192
sound='sound.wav'

def read_signal(filename, winsize):

jfsantos

Traceback (most recent call last):

  File "<string>", line 1, in <module>

  File "/private/var/folders/z3/s0__77pd3n1bpym4f75hrzbc0000gn/T/pip_build_jfsantos/music21/setup.py", line 65, in <module>

    include_package_data=True,

  File "/Users/jfsantos/anaconda/envs/py3/lib/python3.3/distutils/core.py", line 148, in setup

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

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

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

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

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

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