def analyse_rec(sound_files, nsources=1, wind_sec=0.092, min_len=.3,
recognise=None, output_csv='', output_text_grid=''):
# segment recordings
w=[]
for ff in sound_files:
sr,wi=wavread(ff)
w.append(wi.T)
w=np.vstack(w).T
sys.stderr.write("Read {} files, {} channels, {} samples\n"\
.format(len(sound_files),w.shape[1],w.shape[0]))
sys.stderr.write("Segmenting audio\n")
if nsources>1:
seg = MultiChannelSegmenter(w,sr=sr,min_len=min_len)
else:
#w=w.squeeze()
if len(w.shape)>1:
w = np.mean(w,axis=1)
seg = SilenceDetector(w.squeeze(), sr=sr, method = 'pct05',
min_len=min_len, wind_sec=wind_sec)
seg.label = [1 for tst in seg.tst]
seg.centers = np.array([[0,0],[1,0]])
if recognise:
seg.recognise(mode=recognise)
sys.stderr.write("Found {} chunks\n".format(len(seg.label)))
output_results(seg, output_csv=output_csv,
output_text_grid=output_text_grid)
def compare(control_path, exp_path):
"""
Compares two wav files and returns a score. Uses mel frequency ceptrum coefficients as well as dynamic time warping.
:param control_path: the 'correct' wav - what you are comparing to
:param exp_path: the unknown wav
"""
(rate,sig) = wavread(control_path)
(rate2,sig2) = wavread(exp_path)
x = mfcc(sig,rate)
y = mfcc(sig2,rate2)
dist, cost, acc = dtw.dtw(x, y, dist=lambda x, y: dtw.norm(x - y, ord=1))\
return dist
def load_wav(filename):
if filename.endswith('.wav'):
fs, x = wavread(filename)
if fs != 8000:
x = resample(x, int(16000/fs*len(x)))
return x
return np.array([])
def load_vgmwav(wav_fp):
fs, wav = wavread(wav_fp)
assert fs == 44100
if wav.ndim == 2:
wav = wav[:, 0]
wav = wav.astype(np.float32)
wav /= 32767.
return wav
def example():
sig = wavread("ISSpkt.wav")[1]
NRZIa = nc_afskDemod(sig)
fig = plt.figure(figsize=(16,4))
plt.plot(NRZIa)
NRZI = np.sign(NRZIa)
packets ,lastflag = detectFrames(NRZI)
ax = decodeAX25(packets[0])
print("Dest: %s | Source: %s | Digis: %s | %s |" %(ax.destination ,ax.source ,ax.digipeaters,ax.info))
print lastflag
def get_click_sounds():
"""
http://127.0.0.1:5000/get_tabla_sounds
simple! and you get the json data :)
"""
#read a wav sound
output = {}
for stroke in clickStrokes.keys():
fs, data = wavread(clickStrokes[stroke])
output[stroke] = data.tolist()
return jsonify(**output)
def __init__(self, _file, channel=0):
self.ltsa = None
if isinstance(_file, str) and _file[-4:] == '.wav':
self.fs, self.signal = wavread(_file)
if self.signal.ndim > 1:
self.signal = self.signal[:,channel] # take only one channel
else:
raise TypeError('Input is not a path to a .wav file: %s' % str(_file))
self._init_params()
def make_features(wav_dir, mfcc_dir, energy=False, n=13):
if not os.path.exists(mfcc_dir):
os.mkdir(mfcc_dir)
for f in os.listdir(wav_dir):
if f.endswith('.wav'):
fs, w = wavread(wav_dir + '/' + f)
m = mfcc(w, samplerate=fs, appendEnergy=energy, numcep=n)
mean=m.mean(axis=0)
std=m.std(axis=0)
m=(m-mean)/std
np.save(mfcc_dir + '/' + f[:-3] + 'npy', m)
def mixsounds():
"""Return 9 linear mixtures of sound signals.
The sound signals have to be in '.../sources/'.
"""
files = [('../sources/source%i.wav' % i) for i in range(1,10)]
source = np.zeros((50000,9))
for i in range(9):
source[:,i] = wavread(files[i])[1]
source -= np.mean(source, 0)
mix = np.random.rand(9,9)
data = np.dot(source, mix)
return data
def test_decoding():
# Load ISS Packet
Qin = Queue.Queue()
sig = wavread("ISSpkt_full.wav")[1]
print len(sig)
for n in r_[0:len(sig):1024]:
Qin.put(sig[n:n+1024])
Qin.put("END")
length = 43
end = False
count = 1
while(Qin.not_empty):
buf = np.array([])
for i in range(length):
chunk = Qin.get()
if chunk == "END":
print chunk
end = True
break
else:
buf = np.append(buf, chunk)
NRZIa = nc_afskDemod(buf)
NRZI = np.sign(NRZIa)
packets, lastflag = detectFrames(NRZI)
# make recursive?
while(lastflag > 0):
for i in range(20):
chunk = Qin.get()
if chunk == "END":
print chunk
end = True
break
else:
buf = np.append(buf, chunk)
NRZIa = nc_afskDemod(buf)
NRZI = np.sign(NRZIa)
packets, lastflag = detectFrames(NRZI)
if lastflag>0:
print lastflag
for p in packets:
#print "%d. %s"%(count, str(decodeAX25(p)))
ax = decodeAX25(p)
print ("%d. Dest: %s | Source: %s | Digis: %s | %s" %(count, ax.destination ,ax.source , ax.digipeaters, ax.info))
count += 1
if end:
return
def __init__(self, file_path, verbose=False):
if verbose:
print 'Read the audio file:', file_path
try:
sr, sig = wavread(file_path)
except IOError:
print "Error: can\'t read the audio file:", file_path
else:
if verbose:
print '\tSuccessful read of the audio file:', file_path
self.sr = sr
self.sig_int = sig
self.sig_float = pcm2float(sig,dtype='float64')
self.niquist = sr/2
self.file_path = file_path
self.file_name = basename(file_path)
self.filtered = False
self.duration = len(sig)/float(sr)
self.indices = dict() # empty dictionary of Index
def main(**kwargs):
outfile = kwargs['outfile'][0]
infile = kwargs['infile']
print "Filtering %s to %s" % (infile, outfile)
rate, sound_samples = wavread(infile)
mono = True
if 'ndarray' in str(type(sound_samples[0])):
mono = False
# data,r = ffmpeg_load_audio('32but.wav', 44100, True, dtype=np.float32)
rate, sound_samples = ffmpeg_load_audio(infile, rate, mono, dtype=np.float32)
fs = 44100.0
lowcut = 100.0
highcut = 3000.0
# b,a = butter_bandpass(lowcut, highcut, fs, 5)
# filtered = lfilter(b, a, sound_samples)
# filtered = butter_bandpass_filter(sound_samples, lowcut, highcut, fs, 5)
# filtered = butter_bandpass_filter_two(sound_samples, lowcut, highcut, fs, 5)
wavwrite(outfile, rate, sound_samples)
def create_ceps(path): sample_rate, X = wavread(path) ceps, mspec, spec = mfcc(X) write_ceps(ceps, path)
all_electrodes = ((0, ), (0, ), (1, ))
waveform_means = [np.random.randn(30, 1) for _ in range(3)]
for spike_times, electrodes, waveform_mean in \
zip(all_spike_times, all_electrodes, waveform_means):
nwbfile.add_unit(spike_times=spike_times,
electrodes=electrodes,
waveform_mean=waveform_mean)
# analog data
# microphone data
# Be careful! This might contain identifying information
mic_path = '/Users/bendichter/Desktop/Chang/video_abstract/word_emphasis.wav'
mic_fs, mic_data = wavread(mic_path)
nwbfile.add_acquisition(
TimeSeries('microphone',
mic_data,
'audio unit',
rate=float(mic_fs),
description="audio recording from microphone in room"))
# all analog data can be added like the microphone example (speaker, button press, etc.)
spk_path = '/Users/bendichter/Desktop/Chang/video_abstract/word_emphasis.wav'
spk_fs, spk_data = wavread(spk_path)
nwbfile.add_stimulus(
TimeSeries('speaker1',
spk_data,
'audio unit',
rate=float(spk_fs),
description="speaker recording"))
#!/usr/bin/env python
import numpy as np
from scipy.io.wavfile import read as wavread
from scipy.io.wavfile import write as wavwrite
from sklearn.metrics import mean_squared_error
# Print entire, readable ndarrays
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
np.set_printoptions(threshold=np.nan)
f_true, data_true = wavread('umbrella.wav')
f_user, data_user = wavread('cucumberfiltered.wav')
zero_array = np.zeros(3746, dtype=np.float)
data_true = np.concatenate([data_true, zero_array])
print mean_squared_error(data_true, data_user)
fft_true = np.abs(np.fft.fft(data_true))**2
fft_user = np.abs(np.fft.fft(data_user))**2
print mean_squared_error(fft_true, fft_user)
f_true, data_true = wavread('umbrella.wav')
f_user, data_user = wavread('umbrellaonefiltered.wav')
zero_array = np.zeros(4770, dtype=np.float)
data_true = np.concatenate([data_true, zero_array])
def __init__(self, contours, neutral, SHOW_LINGUAGRAM, SHOW_NEUTRAL, SHOW_WAVEFORM, SHOW_SPECTROGRAM):
'''center points determined by transforming the point (426, 393) several times
with peterotron, and taking the average.
'''
self.static_dir = os.getcwd() + '/'
#self.centerX = 710
#self.centerY = 638
# these come from hand tuning to find the smallest range of y values of polar mags
self.centerX = 665
self.centerY = 525
self.gladefile = self.static_dir + "LinguaViewer.glade"
self.wTree = gtk.glade.XML(self.gladefile, "window1")
self.win = self.wTree.get_widget("window1")
self.win.set_title(contours)
self.title = contours
self.mainVBox = self.wTree.get_widget("vbox1")
dic = { "on_window1_destroy": self.onDestroy,
"on_tbPlay_clicked" : self.playSound,
"on_tbSave_clicked" : self.onSave,
"on_tbLabel_clicked": self.onLabel}
self.wTree.signal_autoconnect(dic)
self.X, self.Y = self.loadContours(contours)
self.wavname = contours[:-4] + ".wav"
#Linguagram
if (SHOW_LINGUAGRAM == True):
x1 = array(self.X)
y1 = array(self.Y)
Z = []
for i in range(len(self.X)):
zs = []
for j in range(32):
zs.append(i+1)
Z.append(zs)
z1 = array(Z)
self.fig = Figure()
canvas = FigureCanvas(self.fig)
#ax = Axes3D(self.fig, rect=[-.23,-.2,1.447,1.4])
ax = self.fig.add_subplot(1, 1, 1, projection='3d')
self.fig.subplots_adjust(left=-0.23, bottom=0, right=1.215, top=1)
ax.mouse_init()
surf = ax.plot_surface(z1, -x1, -y1, rstride=1, cstride=1, cmap=cm.jet)
ax.view_init(90,-90)
canvas.show()
canvas.set_size_request(600, 200)
self.mainVBox.pack_start(canvas, True, True)
#Neutral
if (SHOW_NEUTRAL == True):
cx, cy = self.getNeutral(neutral)
cmags = self.makePolar(cx, cy)
M = self.batchConvert2Polar(self.X, self.Y)
#D = self.batchGetMinD(M, cmags)
fakeX = []
for i in range(len(M)):
xs = []
for j in range(1,33):
xs.append(j)
fakeX.append(xs)
x1 = array(fakeX)
y1 = array(M)
Z = []
for i in range(len(M)):
zs = []
for j in range(32):
zs.append(i)
Z.append(zs)
z1 = array(Z)
self.fig3 = Figure()
canvas3 = FigureCanvas(self.fig3)
ax = self.fig3.add_subplot(1, 1, 1, projection='3d')
self.fig3.subplots_adjust(left=-0.23, bottom=0, right=1.215, top=1)
ax.mouse_init()
ax.plot_surface(z1, -x1, y1, rstride=1, cstride=1, cmap=cm.jet)
ax.view_init(90,-90)
canvas3.show()
canvas3.set_size_request(600, 200)
self.mainVBox.pack_start(canvas3, True, True)
#Waveform
windowsize = 0
self.fig2 = Figure()
canvas2 = FigureCanvas(self.fig2)
if (SHOW_WAVEFORM == True):
fs, snd = wavread(self.wavname)
chan = snd[:,0]
t=array(range(len(chan)))/float(fs);
if SHOW_SPECTROGRAM == True:
wavax = self.fig2.add_subplot(2, 1, 1)
else:
wavax = self.fig2.add_subplot(1, 1, 1)
wavax.plot(t,chan,'black');
wavax.set_xlim(0,max(t))
windowsize += 200
#Spectrogram
if (SHOW_SPECTROGRAM == True):
'''This calls Praat to get the spectrogram and adds it to the viewer'''
specname = contours[:-4] + '.Spectrogram'
cleanname = contours[:-4] + '.clean'
cmd = ['/Applications/Praat.app/Contents/MacOS/Praat', self.static_dir + 'makeSpec.praat', self.wavname, specname]
proc = subprocess.Popen(cmd)
status = proc.wait()
cmd2 = ['bash', self.static_dir + 'cleanspec.sh', specname, cleanname]
proc2 = subprocess.Popen(cmd2)
status2 = proc2.wait()
f = open(cleanname, 'r').readlines()
last = len(f)-1
x = f[last].split('\t')
rows = int(x[0])
cols = int(x[1])
img = zeros((rows, cols))
for i in range(len(f)):
x = f[i][:-1].split('\t')
img[int(x[0])-1,int(x[1])-1] = float(x[2])
img = log(img)
if SHOW_WAVEFORM == True:
specax = self.fig2.add_subplot(2, 1, 2)
else:
specax = self.fig2.add_subplot(1, 1, 1)
specax.imshow(img, cmap=cm.gray_r, origin='lower', aspect='auto')
windowsize += 200
# show it
if (SHOW_WAVEFORM == True) or (SHOW_SPECTROGRAM == True):
canvas2.show()
canvas2.set_size_request(600, windowsize)
self.mainVBox.pack_start(canvas2, True, True)
self.SHOW_LINGUAGRAM = SHOW_LINGUAGRAM
self.SHOW_NEUTRAL = SHOW_NEUTRAL
self.SHOW_WAVEFORM = SHOW_WAVEFORM
self.SHOW_SPECTROGRAM = SHOW_SPECTROGRAM
self.windowsize = windowsize
vocoded = lfilter((error_power,), a, vocoded)
vocoded *= hann(len(block))
out[idx:idx+len(block)] += deemphasis(vocoded)
return out
def preemphasis(signal):
return lfilter([1, -0.70], 1, signal)
def deemphasis(signal):
return lfilter([1, 0.70], 1, signal)
def rms(signal):
return sqrt(mean(power(signal, 2)))
if __name__ == "__main__":
fs, data = wavread('Mann.wav')
data = array(data, dtype=double)
data /= amax(absolute(data))
data = decimate(data, 4)
fs = round(fs/4)
block_len = 0.032
overlap = 0.5
order = 16
out = vocode(data, fs, block_len, overlap, order)
wavwrite('vocoded.wav', fs, array(out/amax(absolute(out)) * (2**15-1), dtype=int16))
figure()
plot(data)
# built-in imports
import timeit
# 3rd-party imports
import numpy as np
from scipy.io.wavfile import read as wavread
from scipy.io.wavfile import write
# local imports
from world import main
fs, x_int16 = wavread('test-mwm.wav')
x = x_int16 / (2**15 - 1)
vocoder = main.World()
# profile
print(
timeit.timeit("vocoder.encode(fs, x, f0_method='harvest')",
globals=globals(),
number=1))
'''
# imports
# numpy
import numpy as np
# imports of scipy
from scipy.io.wavfile import read as wavread
from scipy.fftpack import fft
from scipy.signal import lfilter, butter
# graphs
import matplotlib.pyplot as plt
#
from pylab import arange
#
[Fs, samples] = wavread("xmitas02.wav")
#Fs = 150.0; # sampling rate
Ts = 1.0 / Fs
# sampling interval
t = np.arange(0, Fs) # time vector
nyq = 0.5 * Fs # pro filter
# vygeneruj pasmovou propust na 4kHz +- 100Hz
b, a = butter(1, [3900 / nyq, 4100 / nyq], 'bandpass', analog=False)
filtered = lfilter(b, a, samples)
#
plt.plot(t, filtered, 'green', linewidth=.1) # plotting the spectrum
#
down_sample_factor=dsf)
# draw frequecny response
bpf.H0_show(freq_high=20000)
# draw frequecny response, using scipy
bpf.f_show()
# load a sample wav
#path0='wav/400Hz-10dB_44100Hz_400msec.wav'
#path0='wav/1KHz-10dB_44100Hz_400msec.wav'
#path0='wav/3KHz-10dB_44100Hz_400msec.wav'
#path0='wav/5KHz-10dB_44100Hz_400msec.wav'
path0 = 'wav/1KHz-10dB_44100Hz_400ms-TwoTube_stereo.wav'
try:
sr, y = wavread(path0)
except:
print('error: wavread ')
sys.exit()
else:
yg = y / (2**15)
if yg.ndim == 2: # if stereo
yg = np.average(yg, axis=1)
print('sampling rate ', sr)
print('y.shape', yg.shape)
y2 = bpf.filtering(yg) # iir2( yg)
# Exponential Moving Average with Half-wave rectification
ema1 = Class_EMA1()
y3 = ema1(y2)
# display_sample_rate = f.samplerate
# sound_time = f.nframes*1.0/f.samplerate
# sound_data = f.read_frames(f.nframes)
# samples_to_take = int(math.floor(sound_time * display_sample_rate))
# time_step_for_samples = f.samplerate*1.0/display_sample_rate
# wave = []
# for i in xrange(samples_to_take):
# frame_offset = i * time_step_for_samples
# if num_channels == 1:
# wave.append(sound_data[frame_offset])
# else:
# wave.append(sound_data[frame_offset][0])
rate, wave = wavread(infile)
wavwrite('test.wav', rate, wave)
(freq, amp) = get_component_frequencies(wave)
# print type(s)
# with open('data.txt', 'a') as textOutputFile:
# for line in amp:
# textOutputFile.write(str(line))
# textOutputFile.write(',')
# Only plot first 4000 Hz
hz=4000
freq = freq[0:hz]
amp = amp[0:hz]
def read_wav(wav_file):
fr, wav = wavread(wav_file)
wav = wav/np.max(np.abs(wav))
return wav, fr
default='na_1_48k.wav',
help='input wav file')
parser.add_argument('--methodF0',
'-m',
default='harvest',
help='F0 estimation method, harvest or dio ')
parser.add_argument(
'--not_requiem',
action='store_false',
help='use new waveform generator method from WORLD version 0.2.2')
args = parser.parse_args()
# load wav file
wav_path = Path(args.inFILE)
print('input wave path ', wav_path)
fs, x_int16 = wavread(wav_path)
x = x_int16 / (2**15 - 1)
print('fs', fs)
if 0: # resample
fs_new = 16000
x = signal.resample_poly(x, fs_new, fs)
fs = fs_new
if 0: # low-cut
B = signal.firwin(127, [0.01], pass_zero=False)
A = np.array([1.0])
if 0:
import matplotlib.pyplot as plt
w, H = signal.freqz(B, A)
import librosa
import pyrenn
import IPython
import matplotlib.pyplot as plt
# Set the folders
speakers = ['awb','bdl','clb','jmk','ksp','rms','slt']
root = os.getcwd()
folderpath = os.path.join(root,'datasets',speakers[0],'wav')
files = sorted(os.listdir(folderpath))
# Read the files
for file in files:
file = os.path.join(folderpath,file)
fs,audio = wavread(file)
break
# IPython.display.Audio(file)
# YAAPT pitches
signal = basic.SignalObj(file)
pitchY = pYAAPT.yaapt(signal, frame_length=25, frame_space=5, f0_min=40, f0_max=300)
def slice_signal(path, win_len, hop_len, win_frames, hop_frames, sampling_rate,
stream):
slices = []
sr, wavform = wavread(path)
assert sampling_rate == sr
wavform = torch.from_numpy(normalize_wave_minmax(wavform))
stft_complex = torch.stft(wavform, win_len, hop_len)
stft_real_orig, stft_imag_orig = stft_complex[:, :, 0].numpy(
), stft_complex[:, :, 1].numpy()
assert stream in ['in', 'out']
if stream == 'in':
stft_real = in_real_scale(stft_real_orig)
stft_imag = in_imag_scale(stft_imag_orig)
else:
stft_real = out_real_scale(stft_real_orig)
stft_imag = out_imag_scale(stft_imag_orig)
# print(np.max(np.abs(stft_real_recover - stft_real_orig)))
# assert stft_real_recover.all() == stft_real_orig.all()
# assert stft_imag_recover.all() == stft_imag_orig.all()
# stft_real_recover = inverse_in_real_scale(stft_real)
# stft_imag_recover = inverse_in_imag_scale(stft_imag)
#
# stft_recover = np.stack([stft_real_recover, stft_imag_recover], axis=-1)
# signal_recover = torch.istft(torch.from_numpy(stft_recover), n_fft=400, hop_length=160)
# wavwrite('./recover.wav', 16000, signal_recover.numpy())
# stft_orig = np.stack([stft_real_orig, stft_imag_orig], axis=-1)
# signal_orig = torch.istft(torch.from_numpy(stft_orig), n_fft=400, hop_length=160)
# wavwrite('./orig.wav', 16000, signal_orig.numpy())
# stft_real = inverse_out_real_scale(stft_real)
# stft_imag = inverse_out_imag_scale(stft_imag)
# stft = np.stack([np.expand_dims(stft_real, axis=0), np.expand_dims(stft_imag, axis=0)], axis=-1)
len_frames = stft_complex.size()[-2]
num_slices = math.floor((len_frames - win_frames) / hop_frames) + 1
if num_slices > 0:
for idx_slice in range(num_slices):
slices.append([
stft_real[:, idx_slice * hop_frames:idx_slice * hop_frames +
win_frames],
stft_imag[:, idx_slice * hop_frames:idx_slice * hop_frames +
win_frames]
])
# slices_imag.append(stft_imag[:, idx_slice * hop_frames : idx_slice * hop_frames + win_frames].numpy())
# num_slices = len(slices)
# slices_real, slices_imag = [], []
# for idx in range(num_slices):
# slice_real = slices[idx][0][:, 2]
# slice_imag = slices[idx][1][:, 2]
# slices_real.append(slice_real)
# slices_imag.append(slice_imag)
#
# stft_real = np.stack(slices_real)
# stft_imag = np.stack(slices_imag)
#
# stft_real = inverse_out_real_scale(stft_real).T
# stft_imag = inverse_out_imag_scale(stft_imag).T
# stft = np.stack([np.expand_dims(stft_real, axis=0), np.expand_dims(stft_imag, axis=0)], axis=-1)
# wav = torch.istft(torch.from_numpy(stft), 400, 160)
# wavwrite('../save_wav/test2.wav', 16000, wav.numpy().T)
return slices
import tensorflow as tf
import librosa
import scipy
from scipy.io.wavfile import read as wavread
import numpy as np
# load in audio as an array
data, sample_rate = librosa.load('load.wav', sr=None, mono=False)
_, data2 = wavread('load.wav', True)
print(np.max(np.abs(data2-data.T)))
# sample partially
data = data[:100]
# FFT window size to be power of 2, exactly nonoverlapping
chunk_size = 4
# try manual padding of synthesized data
data = np.array([2,6,0,8,1,9,9,5]).astype(np.float32)
# SciPy
_, _, scipy_stft = scipy.signal.stft(data, window='hann', nperseg=chunk_size,
noverlap=chunk_size*3//4, nfft=chunk_size, return_onesided=True,
padded=True, axis=-1)
_, scipy_istft = scipy.signal.istft(scipy_stft, fs=sample_rate, window='hann',
nperseg=chunk_size, noverlap=chunk_size*3//4, nfft=chunk_size, input_onesided=True)
# librosa
rosa_stft = librosa.stft(data, n_fft=chunk_size, hop_length=chunk_size//4,
win_length=chunk_size, window='hann', center=True,
def _get_image(self,
name,
nperseg=126,
noverlap=None,
mag_scale=np.log10(2**15)):
"""
From audio in the file name construct the magnitude/phase tensor.
Parameters
----------
name : string
Name of the audio file.
nperseg : int
Size of each FFT window for the STFT.
noverlap : int or None
Size of the overlap to the STFT. If None, then a half-step is
used.
mag_scale : float
Value with which the magnitude will be scaled.
Returns
-------
mag_phase : ndarray, shape (stft_width, stft_heigth, 2)
The magnitude/phase tensor.
"""
# Read the audio and downscale the rate by 2
rate, audio_sig = wavread(self.im_dir + name)
rate, audio_sig = self._downsample(rate, audio_sig)
# Set global rate
if self.rate is None:
self.rate = rate
# Right pad audio to desired size
if noverlap is None:
noverlap = (nperseg + 1) // 2
length_orig = len(audio_sig)
length_pad = int(np.ceil(length_orig / noverlap) * noverlap)
audio_sig = np.pad(audio_sig, (0, length_pad - length_orig),
'constant')
# Make a Short time Fourier transform
frequencies, times, stft = signal.stft(audio_sig,
fs=rate,
nperseg=nperseg,
noverlap=noverlap)
# Convert to log10 magnitude and phase
spectrogram = np.log10(np.absolute(stft) + 1e-10)
phasegram = np.angle(stft) / np.pi # Scale angles to [-1, 1]
# Scale the magnitude
spectrogram /= mag_scale
if stft.shape[1] != 128:
# Pad the matrices
spectrogram = np.pad(spectrogram,
[(0, 0), (0, 128 - stft.shape[1])], 'minimum')
phasegram = np.pad(phasegram, [(0, 0), (0, 128 - stft.shape[1])],
'constant')
# Join into a two-channel tensor
return np.stack((phasegram, spectrogram), axis=-1)
import os
from os.path import join as pjoin
from scipy.io.wavfile import read as wavread, write as wavwrite
clean_dir = '/nas/staff/data_work/Sure/Edinburg_Speech/clean_testset_wav_16k'
noisy_dir = '/nas/staff/data_work/Sure/Edinburg_Speech/noisy_testset_wav_16k'
noise_dir = '/nas/staff/data_work/Sure/Edinburg_Speech/noise_testset_wav_16k'
filenames = os.listdir(clean_dir)
num_filenames = len(filenames)
file_counter = 0
for filename in filenames:
file_counter += 1
print('Processing audio file [{}/{}]: {}'.format(file_counter,
num_filenames, filename))
clean_path = pjoin(clean_dir, filename)
noisy_path = pjoin(noisy_dir, filename)
noise_path = pjoin(noise_dir, filename)
fs, clean_waveform = wavread(clean_path)
_, noisy_waveform = wavread(noisy_path)
noise_waveform = noisy_waveform - clean_waveform
wavwrite(noise_path, fs, noise_waveform)
#!/usr/bin/env python
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from scipy.io.wavfile import read as wavread
from scipy.io.wavfile import write as wavwrite
# Print entire, readable ndarrays
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
np.set_printoptions(threshold=np.nan)
f, data = wavread('test.wav')
ps = np.abs(np.fft.fft(data))**2
time_step = 1 / 44100
freqs = np.fft.fftfreq(data.size, time_step)
idx = np.argsort(freqs)
print ps
plt.plot(freqs, ps)
plt.show()
def show_spectrogram(path):
sample_rate, X = wavread(path)
output = specgram(X, Fs=sample_rate)
# # # quando necessário é plotado o áudio do arquivo
# # t = np.linspace(0, N/fs, N)
# # plt.plot(t, data_file)
# MFCCsample = librosa.feature.mfcc(y=frameSample, sr=fs, fmin=fmin, fmax=fmax,
# n_mfcc=n_mfcc, n_mels=n_mels, n_fft=n_fft)
# frameMFCC[j] = MFCCsample[ofs_mfcc:]
# kwFeat[i] = frameMFCC
frameMFCC = {}
kwFeat = {}
for i in range(len(df['file'])):
wavstr = df['file'][
i] # extrai a string contendo o nome do arquivo de audio
[_, data_file] = wavread('../../' + wavstr) # Lê todo o arquivo de audio
data_file = data_file / 32767 # normaliza as amostras do áudio para o range [-1,1]
N = data_file.shape[0] # indica o tamanho do arquivo
# # quando necessário é plotado o áudio do arquivo
# t = np.linspace(0, N/fs, N)
# plt.plot(t, data_file)
for j in range(len(
iAbre[i])): # para cada audio, retira os frames kw e as features
if iAbre[i][j] < 0:
break
frameSample = data_file[iAbre[i][j] - frame_lenD2:iAbre[i][j] +
frame_lenD2]
MFCCsample = librosa.feature.mfcc(y=frameSample,
if len(data.shape) == 1 and self.output_channels != 1:
# replicate first channel and broadcast to (chan, 1)
data = np.tile(data, (self.output_channels, 1)).T
if data.shape != (num_frames, self.output_channels):
error = 'Can not broadcast array of shape {} to {}'.format(
data.shape, (num_frames, self.output_channels))
raise ValueError(error)
data = data.flatten().tostring()
err = _pa.Pa_WriteStream(self._stream[0], data, num_frames)
self._handle_error(err)
if __name__ == '__main__':
from scipy.io.wavfile import read as wavread
import time
fs, wave = wavread('thistle.wav')
wave = np.array(wave, dtype=np.float32)
wave /= 2**15
block_length = 4
def callback(in_data, frame_count, time_info, status):
if status != 0:
print(status)
return (in_data, continue_flag)
s = Stream(sample_rate=fs, block_length=block_length, callback=callback)
s.start()
# for n in range(int(fs*5/block_length)):
# s.write(s.read(block_length))
# for idx in range(0, wave.size, block_length):
# s.write(wave[idx:idx+block_length])
time.sleep(5)
s.stop()
current_hop = resolution * round(float(current_hop)/resolution)
return indicies, ideal_vals
if __name__ == '__main__':
from scipy.io.wavfile import read as wavread
import matplotlib.pyplot as plt
#'''
fs, y = wavread('New Seal and New Spring_conv.wav')
#fs, y = wavread('equation9sec.wav')
y = y[...,0]
t = y.shape[0] / np.float32(fs)
#'''
'''
f0 = 440
fs = 48000
t = 5
n = np.arange(fs*t)
y = 0.5*np.cos(2*np.pi*f0*n/float(fs))
'''
import sys
import time
import numpy as np
from scipy.io.wavfile import read as wavread
from pysoundcard import Stream, continue_flag, complete_flag
"""Play an audio file."""
fs, wave = wavread(sys.argv[1])
wave = np.array(wave, dtype=np.float32)
wave /= 2 ** 15 # normalize -max_int16..max_int16 to -1..1
play_position = 0
def callback(in_data, out_data, time_info, status):
global play_position
out_data[:] = wave[play_position:play_position + block_length]
# TODO: handle last (often incomplete) block
play_position += block_length
if play_position + block_length < len(wave):
return continue_flag
else:
return complete_flag
block_length = 16
s = Stream(sample_rate=fs, block_length=block_length, callback=callback)
s.start()
while s.is_active():
time.sleep(0.1)
def inception_score(audio_fps,
k,
metagraph_fp,
ckpt_fp,
batch_size=100,
tf_ffmpeg_ext=None,
fix_length=False):
use_tf_ffmpeg = tf_ffmpeg_ext is not None
if not use_tf_ffmpeg:
from scipy.io.wavfile import read as wavread
if len(audio_fps) % k != 0:
raise Exception(
'Number of audio files ({}) is not divisible by k ({})'.format(
len(audio_fps), k))
group_size = len(audio_fps) // k
# Restore graph
graph = tf.Graph()
with graph.as_default():
saver = tf.train.import_meta_graph(metagraph_fp)
if use_tf_ffmpeg:
x_fp = tf.placeholder(tf.string, [])
x_bin = tf.read_file(x_fp)
x_samps = tf.contrib.ffmpeg.decode_audio(x_bin, tf_ffmpeg_ext,
16000, 1)[:, 0]
x = graph.get_tensor_by_name('x:0')
scores = graph.get_tensor_by_name('scores:0')
# Restore weights
sess = tf.Session(graph=graph)
saver.restore(sess, ckpt_fp)
# Evaluate audio
_all_scores = []
for i in xrange(0, len(audio_fps), batch_size):
batch = audio_fps[i:i + batch_size]
# Load audio files
_xs = []
for audio_fp in batch:
if use_tf_ffmpeg:
_x = sess.run(x_samps, {x_fp: audio_fp})
else:
fs, _x = wavread(audio_fp)
if fs != 16000:
raise Exception('Invalid sample rate ({})'.format(fs))
if _x.dtype == np.int16:
_x = _x.astype(np.float32)
_x /= 32767.
if _x.ndim != 1:
raise Exception('Invalid shape ({})'.format(_x.shape))
if fix_length:
_x = _x[:16384]
#_x = _x[-16384:]
_x = np.pad(_x, (0, 16384 - _x.shape[0]), 'constant')
if _x.shape[0] != 16384:
raise Exception('Invalid number of samples ({})'.format(
_x.shape[0]))
_xs.append(_x)
# Compute model scores
_all_scores.append(sess.run(scores, {x: _xs}))
sess.close()
# Find labels
_all_scores = np.concatenate(_all_scores, axis=0)
_all_labels = np.argmax(_all_scores, axis=1)
# Compute inception scores
_inception_scores = []
for i in xrange(k):
_group = _all_scores[i * group_size:(i + 1) * group_size]
_kl = _group * (np.log(_group) -
np.log(np.expand_dims(np.mean(_group, 0), 0)))
_kl = np.mean(np.sum(_kl, 1))
_inception_scores.append(np.exp(_kl))
return np.mean(_inception_scores), np.std(_inception_scores), _all_labels
def __init__(self, path0): # , sampling_rate=48000):
# initalize
sr, y = wavread(path0)
self.yg= y / (2 ** 15)
self.sr= sr
print ('sampling rate ', sr)
References
----------
.. [1] S.B. Davis and P. Mermelstein, "Comparison of parametric
representations for monosyllabic word recognition in continuously
spoken sentences", IEEE Trans. Acoustics. Speech, Signal Proc.
ASSP-28 (4): 357-366, August 1980."""
https://www.researchgate.net/publication/261914482_Feature_Extraction_Methods_LPC_PLP_and_MFCC_In_Speech_Recognition
Source Code
-----------
https://github.com/cournape/talkbox
'''
from scipy.io.wavfile import read as wavread
from scikits.talkbox.features import mfcc
from scikits.talkbox.linpred.levinson_lpc import *
# data: raw audio data
# fs: sample rate
sr, signal = wavread('../recordings/obama.wav')
# ceps: cepstral cofficients
coeffs=13
ceps, mspec, spec = mfcc(signal, nwin=2048, nfft=2048, fs=sr, nceps=coeffs)
print ("************************ MFCC ************************")
print (ceps)
# https://github.com/cournape/talkbox/blob/ee0ec30a6a6d483eb9284f72bdaf26bd99765f80/scikits/talkbox/linpred/levinson_lpc.py
lpcResult = lpc(signal,1)
print ("************************ LPC ************************")
print (lpcResult)
import sys
import numpy as np
from scipy.io.wavfile import read as wavread
from pysoundcard import Stream
"""Play an audio file."""
fs, wave = wavread(sys.argv[1])
wave = np.array(wave, dtype=np.int16)
blocksize = 256
s = Stream(samplerate=fs, blocksize=blocksize, dtype='int16')
s.start()
while True:
s.write(wave[0:(1024 * 100)])
s.stop()
def read_as_mfcc(path): sample_rate, X = wavread(path) ceps, mspec, spec = mfcc(X) return ceps
import numpy as np
from scipy.io.wavfile import read as wavread
x = np.loadtxt("out.txt")
fs, audio = wavread("test.wav")
audio = audio / (2**15)
print("Error:", np.mean(np.abs(x - audio)))
def __init__(self, contours, neutral, SHOW_LINGUAGRAM, SHOW_NEUTRAL,
SHOW_WAVEFORM, SHOW_SPECTROGRAM):
'''center points determined by transforming the point (426, 393) several times
with peterotron, and taking the average.
'''
self.static_dir = os.getcwd() + '/'
#self.centerX = 710
#self.centerY = 638
# these come from hand tuning to find the smallest range of y values of polar mags
self.centerX = 665
self.centerY = 525
self.gladefile = self.static_dir + "LinguaViewer.glade"
self.wTree = gtk.glade.XML(self.gladefile, "window1")
self.win = self.wTree.get_widget("window1")
self.win.set_title(contours)
self.title = contours
self.mainVBox = self.wTree.get_widget("vbox1")
dic = {
"on_window1_destroy": self.onDestroy,
"on_tbPlay_clicked": self.playSound,
"on_tbSave_clicked": self.onSave,
"on_tbLabel_clicked": self.onLabel
}
self.wTree.signal_autoconnect(dic)
self.X, self.Y = self.loadContours(contours)
self.wavname = contours[:-4] + ".wav"
#Linguagram
if (SHOW_LINGUAGRAM == True):
x1 = array(self.X)
y1 = array(self.Y)
Z = []
for i in range(len(self.X)):
zs = []
for j in range(32):
zs.append(i + 1)
Z.append(zs)
z1 = array(Z)
self.fig = Figure()
canvas = FigureCanvas(self.fig)
#ax = Axes3D(self.fig, rect=[-.23,-.2,1.447,1.4])
ax = self.fig.add_subplot(1, 1, 1, projection='3d')
self.fig.subplots_adjust(left=-0.23, bottom=0, right=1.215, top=1)
ax.mouse_init()
surf = ax.plot_surface(z1,
-x1,
-y1,
rstride=1,
cstride=1,
cmap=cm.jet)
ax.view_init(90, -90)
canvas.show()
canvas.set_size_request(600, 200)
self.mainVBox.pack_start(canvas, True, True)
#Neutral
if (SHOW_NEUTRAL == True):
cx, cy = self.getNeutral(neutral)
cmags = self.makePolar(cx, cy)
M = self.batchConvert2Polar(self.X, self.Y)
#D = self.batchGetMinD(M, cmags)
fakeX = []
for i in range(len(M)):
xs = []
for j in range(1, 33):
xs.append(j)
fakeX.append(xs)
x1 = array(fakeX)
y1 = array(M)
Z = []
for i in range(len(M)):
zs = []
for j in range(32):
zs.append(i)
Z.append(zs)
z1 = array(Z)
self.fig3 = Figure()
canvas3 = FigureCanvas(self.fig3)
ax = self.fig3.add_subplot(1, 1, 1, projection='3d')
self.fig3.subplots_adjust(left=-0.23, bottom=0, right=1.215, top=1)
ax.mouse_init()
ax.plot_surface(z1, -x1, y1, rstride=1, cstride=1, cmap=cm.jet)
ax.view_init(90, -90)
canvas3.show()
canvas3.set_size_request(600, 200)
self.mainVBox.pack_start(canvas3, True, True)
#Waveform
windowsize = 0
self.fig2 = Figure()
canvas2 = FigureCanvas(self.fig2)
if (SHOW_WAVEFORM == True):
fs, snd = wavread(self.wavname)
chan = snd[:, 0]
t = array(range(len(chan))) / float(fs)
if SHOW_SPECTROGRAM == True:
wavax = self.fig2.add_subplot(2, 1, 1)
else:
wavax = self.fig2.add_subplot(1, 1, 1)
wavax.plot(t, chan, 'black')
wavax.set_xlim(0, max(t))
windowsize += 200
#Spectrogram
if (SHOW_SPECTROGRAM == True):
'''This calls Praat to get the spectrogram and adds it to the viewer'''
specname = contours[:-4] + '.Spectrogram'
cleanname = contours[:-4] + '.clean'
cmd = [
'/Applications/Praat.app/Contents/MacOS/Praat',
self.static_dir + 'makeSpec.praat', self.wavname, specname
]
proc = subprocess.Popen(cmd)
status = proc.wait()
cmd2 = [
'bash', self.static_dir + 'cleanspec.sh', specname, cleanname
]
proc2 = subprocess.Popen(cmd2)
status2 = proc2.wait()
f = open(cleanname, 'r').readlines()
last = len(f) - 1
x = f[last].split('\t')
rows = int(x[0])
cols = int(x[1])
img = zeros((rows, cols))
for i in range(len(f)):
x = f[i][:-1].split('\t')
img[int(x[0]) - 1, int(x[1]) - 1] = float(x[2])
img = log(img)
if SHOW_WAVEFORM == True:
specax = self.fig2.add_subplot(2, 1, 2)
else:
specax = self.fig2.add_subplot(1, 1, 1)
specax.imshow(img, cmap=cm.gray_r, origin='lower', aspect='auto')
windowsize += 200
# show it
if (SHOW_WAVEFORM == True) or (SHOW_SPECTROGRAM == True):
canvas2.show()
canvas2.set_size_request(600, windowsize)
self.mainVBox.pack_start(canvas2, True, True)
self.SHOW_LINGUAGRAM = SHOW_LINGUAGRAM
self.SHOW_NEUTRAL = SHOW_NEUTRAL
self.SHOW_WAVEFORM = SHOW_WAVEFORM
self.SHOW_SPECTROGRAM = SHOW_SPECTROGRAM
self.windowsize = windowsize
def PMBSegmentation(argv, nameFileOutputXML):
inputPath = None
outputPath = 'out.lab'
boundariesPath = None
verbose = False
# Communs
wLen = 0.016
wStep = 0.008
withEntropy = False
with4Hz = False
withNBS = False
withLS = False
moduLen = 1
speech_labels = {0: 'Non Speech', 1: 'Speech'}
music_labels = {0: 'Non Music', 1: 'Music'}
sort = False
# entropy
entropyTh = 0.4
# 4 Hz
fcenter = 4.0
fwidth = 0.5
normalized = True
N = 2048
ordre = 100
nbFilters = 30
energyTh = 1.5
# Music
musicLen = 1.0
musicStep = 0.1
maxSegForLength = 1000
thLen = 0.04
thNb = 20
segments = []
boundaries = None
# Lecture des arguments
opts = argv
#print opts
i = 0
while (i < len(argv)):
#print str(i)
if opts[i] == '-h':
printhelp()
elif opts[i] == '-i':
i = i + 1
inputPath = opts[i]
elif opts[i] == '-o':
outputPath = opts[i]
elif opts[i] == '-b':
i = i + 1
boundariesPath = opts[i]
elif opts[i] == '-v':
verbose = True
elif opts[i] == '--sorted':
sort = True
elif opts[i] == '--Entropy':
withEntropy = True
elif opts[i] == '--4Hz':
with4Hz = True
elif opts[i] == '--NBS':
withNBS = True
elif opts[i] == '--LS':
withLS = True
elif opts[i] == '-w':
i = i + 1
wLen = float(opts[i])
elif opts[i] == '-s':
i = i + 1
wStep = float(opts[i])
i = i + 1
if inputPath == None:
printhelp()
exit(1)
else:
#print "Audio file path : "+ inputPath
fe, data = wavread(inputPath)
print "Audio file opened : " + inputPath
fe = float(fe)
m = iinfo(data[0]).max
data = [float(d) / m for d in data]
demi = int(wLen / 2 * fe)
timeScale = range(demi, len(data) - demi, int(wStep * fe))
frames = [data[t - demi:t + demi] for t in timeScale]
if withEntropy:
if verbose:
print 'Analyse de la modulation d\'entropy'
entropy_values = [entropy(f) for f in frames]
entropy_modulation = computeModulation(entropy_values,
moduLen / wStep,
withLog=False)
with open('entropy.lab', 'w') as f:
for t, v in zip(timeScale, entropy_modulation):
f.write('%f\t%f\n' % (float(t) / fe, v))
entropy_modulation = [(e / entropyTh) - 1 if e < 2 * entropyTh else 1
for e in entropy_modulation]
segments_entropy = decoupe(entropy_modulation)
segments_entropy = [(s[0] * wStep, s[1] * wStep,
speech_labels[s[2]] + ' (Entropy)')
for s in segments_entropy]
segments.extend(segments_entropy)
if with4Hz:
if verbose:
print 'Analyse de la modulation d\'energie a 4Hz'
Wo = fcenter / fe
Wn = [Wo - (fwidth / 2) / fe, Wo + (fwidth / 2) / fe]
num = firwin(ordre, Wn, pass_zero=False)
melFilter = melFilterBank(nbFilters, N, fe)
hw = hamming(wLen * fe)
energy = [
dot(abs(rfft(hw * f, n=2 * N)[0:N])**2, melFilter) for f in frames
]
# transposition de list of list
energy = lfilter(num, 1, map(list, zip(*energy)), 0)
energy = sum(energy)
if normalized:
energy = energy / mean(energy)
energy_modulation = computeModulation(energy,
moduLen / wStep,
withLog=True)
with open('energy.lab', 'w') as f:
for t, v in zip(timeScale, energy_modulation):
f.write('%f\t%f\n' % (float(t) / fe, v))
energy_modulation = [(e / energyTh) - 1 if e < 2 * energyTh else 1
for e in energy_modulation]
segments_energy = decoupe(energy_modulation)
segments_energy = [(s[0] * wStep, s[1] * wStep,
speech_labels[s[2]] + ' (4Hz)')
for s in segments_energy]
segments.extend(segments_energy)
if withLS:
if verbose:
print 'Analyse de la longueur des segments'
if boundariesPath == None:
a, b = segment(data, fe)
boundaries = [(float(st[0]) / fe, ) for st in a]
else:
boundaries = readBoundaries(boundariesPath)
times = array([b[0] for b in boundaries])
demi = musicLen / 2
timeScale = arange(demi, times[-1] - demi, musicStep)
# On prend les plus petits !!
segframes = [
sorted(diff(times[logical_and(times >= t - demi,
times <= t + demi)]),
reverse=True) for t in timeScale
]
lengths = [mean(s[:min([maxSegForLength, len(s)])]) for s in segframes]
with open('LS.lab', 'w') as f:
for t, v in zip(timeScale, lengths):
f.write('%f\t%f\n' % (float(t) / fe, v))
lengths = [(l / thLen) - 1 if l < 2 * thLen else 1 for l in lengths]
segments_length = decoupe(lengths)
segments_length = [(s[0] * musicStep, s[1] * musicStep,
music_labels[s[2]] + ' (LS)')
for s in segments_length]
segments.extend(segments_length)
if withNBS:
if verbose:
print 'Analyse du nombre de segments'
if boundariesPath == None:
if boundaries == None:
a, b = segment(data, fe)
boundaries = [(float(st[0]) / fe, ) for st in a]
else:
if boundaries == None:
boundaries = readBoundaries(boundariesPath)
times = array([b[0] for b in boundaries])
demi = musicLen / 2
timeScale = arange(demi, times[-1] - demi, musicStep)
segnb = [
float(npSum(logical_and(times >= t - demi, times <= t + demi)))
for t in timeScale
]
with open('NBS.lab', 'w') as f:
for t, v in zip(timeScale, segnb):
f.write('%f\t%f\n' % (float(t) / fe, v))
segnb = [-(l / thNb) + 1 if l < 2 * thNb else 1 for l in segnb]
segments_nb = decoupe(segnb)
segments_nb = [(s[0] * musicStep, s[1] * musicStep,
music_labels[s[2]] + ' (NBS)') for s in segments_nb]
segments.extend(segments_nb)
if sort:
segments = sorted(segments, key=lambda x: x[0])
v = writeToXML(segments, nameFileOutputXML, withNBS, with4Hz, withLS,
withEntropy)
return v
#print "Audio file processed sucessfully"
'''
wav_fps = wav_fps[:args.n]
# Graph to calculate feats
x = tf.placeholder(tf.float32, [None])
x_trim = x[:16384]
x_trim = tf.pad(x_trim, [[0, 16384 - tf.shape(x_trim)[0]]])
X = tf.contrib.signal.stft(x_trim, 2048, 128, pad_end=True)
X_mag = tf.abs(X)
W_mel = tf.contrib.signal.linear_to_mel_weight_matrix(
num_mel_bins=128,
num_spectrogram_bins=1025,
sample_rate=16000,
lower_edge_hertz=40.,
upper_edge_hertz=7800.,
)
X_mel = tf.matmul(X_mag, W_mel)
X_lmel = tf.log(X_mel + 1e-6)
X_feat = X_lmel
# Calculate feats for each wav file
with tf.Session() as sess:
_X_feats = []
for wav_fp in tqdm(wav_fps):
_, _x = wavread(wav_fp)
_X_feats.append(sess.run(X_feat, {x: _x}))
_X_feats = np.array(_X_feats)
with open(args.out_fp, 'wb') as f:
pickle.dump(_X_feats, f)
from scipy.io.wavfile import read as wavread
file_name = "quantized/piano/single/mono_sound/piano_mono_2.wav"
point = 44248
length = 100
rate, data = wavread(file_name)
print("rate", rate)
print("data", data.shape)
print("expected", rate * 4)
left = data[:, 1].tolist()
episode = left[point:point + length]
def find_subpattern(lst, pattern):
indices = []
for i in range(len(lst) - length):
if lst[i:i + length] == pattern:
indices.append(i)
return indices
indexs = find_subpattern(left, episode)
start = indexs[0]
for i in range(len(indexs) - 1):
indexs[i + 1] = indexs[i + 1] - start - 44100 * i
print("indexs", indexs)
#!/usr/bin/python
import sys
import argparse
import math
import numpy as np
from scipy.io.wavfile import read as wavread
from scipy.io.wavfile import write as wavwrite
# Print entire, readable ndarrays
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
np.set_printoptions(threshold=np.nan)
rate_one, data_one = wavread('umbrellaonefiltered.wav')
rate_two, data_two = wavread('umbrellatwofiltered.wav')
zero_count = int(math.floor(len(data_one) / 2))
zero_count_two = int(math.floor(len(data_two) / 2))
if zero_count_two > zero_count:
zero_count = zero_count_two
# Zero pad file one
zero_array = np.zeros(zero_count, dtype=np.float)
data_one = np.concatenate([data_one, zero_array])
length_one = int(math.floor(len(data_one)))
# FFT file one
fft_one = np.fft.rfft(data_one)
# Zero pad file two
length_two = int(math.floor(len(data_two)))
plt.grid()
fig.tight_layout()
plt.show()
if __name__ == '__main__':
from scipy.io.wavfile import read as wavread
# instance
w = Class_Wavelet1()
# load wav sample
path = 'sample1.wav'
sr, x = wavread(path)
# (1) show back to original waveform by transform and inverse transform
# select switch (filter): if value is 0.0: then doesn't use the element
# s1, s2, s3, s4, s5, s6, s7, s8
flt = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] # use all elements
lng0 = 2048 # set length of wavelet transform
y = w.trans_itrans_level8(x[0:lng0], filter=flt, show=True)
# (2) show comparison with composition from selected elements only
# select switch (filter): if value is 0.0: then doesn't use the element
# s1, s2, s3, s4, s5, s6, s7, s8
flt = [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0,
1.0] # only s6, s7, and s8 are used
lng0 = 2048
y = w.trans_itrans_level8(x[0:lng0], filter=flt, show=True)
