import matplotlib.pyplot as plot
import WAV as wav
import STFT as stft
from Oscillator import *
# Load sample input...
# x, sr = wav.read("wav/tomsawyer.wav")
# or produce cos wave
# sr = 44100.0
# osc = Oscillator(sr, 440.0)
# x = 0.5 * osc.getBuffer(123456)
# Set STFT parameters
winSize = 1024*2 # in samples
hopSize = winSize/4 # in samples
# Build output signal
frames = stft.stft(x, winSize, hopSize)
y = stft.istft(frames, winSize, hopSize)
# Plot x-y difference
n = min(len(x), len(y))
diff = abs(x[0:n] - y[0:n])
plot.figure()
plot.plot(diff, "b")
plot.show()
# Write out results
wav.write(x, sr, "wav/stft_input.wav")
wav.write(y, sr, "wav/stft_output.wav")
import matplotlib.pyplot as plot
import WAV as wav
import STFT as stft
from Oscillator import *
# Load sample input...
# x, sr = wav.read("wav/tomsawyer.wav")
# or produce cos wave
# sr = 44100.0
# osc = Oscillator(sr, 440.0)
# x = 0.5 * osc.getBuffer(123456)
# Set STFT parameters
winSize = 1024 * 2 # in samples
hopSize = winSize / 4 # in samples
# Build output signal
frames = stft.stft(x, winSize, hopSize)
y = stft.istft(frames, winSize, hopSize)
# Plot x-y difference
n = min(len(x), len(y))
diff = abs(x[0:n] - y[0:n])
plot.figure()
plot.plot(diff, "b")
plot.show()
# Write out results
wav.write(x, sr, "wav/stft_input.wav")
wav.write(y, sr, "wav/stft_output.wav")
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
wav.write(x, sr, "wav/automute_input.wav")
wav.write(y, sr, "wav/automute_output.wav")
plot.savefig("wav/test/Results/" + filename + ".pdf", dpi=600)
wav.write(y, sr, "wav/test/Results/" + filename)
# 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
wav.write(x, sr, "wav/automute_input.wav")
wav.write(y, sr, "wav/automute_output.wav")
# plot.savefig("wav/test/Results/" + filename + ".pdf", dpi=600)
# wav.write(y, sr, "wav/test/Results/" + filename)
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")
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
wav.write(x, sr, "wav/automute_input.wav")
wav.write(y, sr, "wav/automute_output.wav")
# plot.savefig("wav/test/Results/" + filename + ".pdf", dpi=600)
# wav.write(y, sr, "wav/test/Results/" + filename)
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
wav.write(x, sr, "wav/automute_input.wav")
wav.write(y, sr, "wav/automute_output.wav")
plot.savefig("wav/test/Results/" + filename + ".pdf", dpi=600)
wav.write(y, sr, "wav/test/Results/" + filename)
