from numpy import *
import matplotlib.pyplot as plot
import WAV as wav
from VAD import vad
import scipy
# Load sample input
x, sr = wav.read("wav/test/test_bh_raw.wav")
# Set VAD parameters
winSize = int(sr*20e-3) # in samples (fs*s)
hopSize = winSize # in samples
smoothingGain = [0.3, 0.02] # small numbers TODO: calculte for 0-values 0.1, 0.2, 0.3
triggerThresholds = [-25, -20] # in dBFS
# Estimate silent frames
vadFlags = vad(x, winSize, hopSize, smoothingGain, triggerThresholds)
# Plot results
plot.figure()
plot.plot(x, "b")
#plot.plot(range(0, len(x)-winSize, hopSize), vadFlags*max(x), "r")
plot.plot(range(0, len(x)-winSize, hopSize), vadFlags[:,1], "r", linewidth=2) # TEST
plot.plot(range(0, len(x)-winSize, hopSize), vadFlags[:,2], "b")
plot.plot(range(len(x)), triggerThresholds[0]*ones(len(x)), "g--")
plot.plot(range(len(x)), triggerThresholds[1]*ones(len(x)), "g--")
plot.show()
from numpy import *
import matplotlib.pyplot as plot
import WAV as wav
from VAD import vad
import scipy
# Load sample input
x, sr = wav.read("wav/test/test_bh_raw.wav")
# Set VAD parameters
winSize = int(sr * 20e-3) # in samples (fs*s)
hopSize = winSize # in samples
smoothingGain = [0.3, 0.02
] # small numbers TODO: calculte for 0-values 0.1, 0.2, 0.3
triggerThresholds = [-25, -20] # in dBFS
# Estimate silent frames
vadFlags = vad(x, winSize, hopSize, smoothingGain, triggerThresholds)
# Plot results
plot.figure()
plot.plot(x, "b")
#plot.plot(range(0, len(x)-winSize, hopSize), vadFlags*max(x), "r")
plot.plot(range(0,
len(x) - winSize, hopSize),
vadFlags[:, 1],
"r",
linewidth=2) # TEST
plot.plot(range(0, len(x) - winSize, hopSize), vadFlags[:, 2], "b")
plot.plot(range(len(x)), triggerThresholds[0] * ones(len(x)), "g--")
plot.plot(range(len(x)), triggerThresholds[1] * ones(len(x)), "g--")
from numpy import *
import matplotlib.pyplot as plot
import WAV as wav
from VAD import vad, mute
filename = "tomsawyer.wav"
# Load sample input
x, sr = wav.read("wav/" + filename)
# Set VAD parameters
winSize = int(sr*20e-3) # in samples (fs*s)
hopSize = winSize # in samples
smoothingGain = [0.3, 0.02] # small numbers
triggerThresholds = [-25, -20] # in dBFS
# Estimate silent frames
vadFlags = vad(x, winSize, hopSize, smoothingGain, triggerThresholds)
# Mute silent frames
y = mute(x, winSize, hopSize, vadFlags)
# Plot results
plot.figure()
plot.plot(x, "b")
plot.plot(y, "r")
plot.plot(range(0, len(y)-winSize, hopSize), vadFlags[:,1], "g--")
plot.legend(("Input signal", "Output signal", "Signal offset"))
plot.show()
# Write out results
from numpy import *
from Oscillator import *
#import matplotlib.pyplot as plot
import WAV as wav
[input, sr] = wav.read('wav/x1.wav')
oscillator = Oscillator(sr, 1000.0)
wave = oscillator.getBuffer(len(input))
output = multiply(input, wave)
#wav.sound(input, sr, "/tmp/input.wav")
wav.sound(output, sr, "/tmp/output.wav")
from numpy import *
import WAV as wav
from Oscillator import *
# Load sample input
[x, sr] = wav.read('wav/tomsawyer.wav')
# Make cos wave
osc = Oscillator(sr, 500.0)
f = osc.getBuffer(len(x))
# Shift frequency by f Hz
y = multiply(x, f)
# Write out results
wav.write(x, sr, "wav/freqshift_input.wav")
wav.write(y, sr, "wav/freqshift_output.wav")
from numpy import *
import matplotlib.pyplot as plot
import WAV as wav
from VAD import vad, mute
filename = "tomsawyer.wav"
# Load sample input
x, sr = wav.read("wav/" + filename)
# Set VAD parameters
winSize = int(sr * 20e-3) # in samples (fs*s)
hopSize = winSize # in samples
smoothingGain = [0.3, 0.001] # small numbers
triggerThresholds = [-25, -20] # in dBFS
# Estimate silent frames
vadFlags = vad(x, winSize, hopSize, smoothingGain, triggerThresholds)
# Mute silent frames
y = mute(x, winSize, hopSize, vadFlags)
# Plot results
plot.figure()
plot.plot(x, "b")
plot.plot(y, "r")
plot.plot(range(0, len(y) - winSize + 1, hopSize), vadFlags[:, 1], "g--")
plot.legend(("Input signal", "Output signal", "Signal offset"))
plot.show()
# Write out results
from numpy import *
import matplotlib.pyplot as plot
import WAV as wav
def rotate(array, n=1):
n = n % len(array)
return array[n:] + array[:n]
def rms(array):
result = 0
for i in range(len(array)):
result += array[i] * array[i]
return result # sqrt(result/len(array))
# Input data
x = wav.read("samples/test.wav")
x = x[0]
# Output data
y = zeros(len(x))
# Init RMS buffer
rmsBufferSize = 10;
rmsBuffer = zeros(rmsBufferSize);
# Fill output data
for i in range(len(x)):
rmsBuffer[0] = x[i]
y[i] = rms(rmsBuffer)
bla = rotate(rmsBuffer)