Python wavread示例

示例#1

def ReadWavFile(filename):
    ''' Read wav data from the file at filename. '''
    return M.wavread(filename)

示例#2

文件： audio_segment_test.py 项目： cjacoby/mir-noise

import os
import numpy as np
import marlib.matlab as M
import mirlib.feature_extraction.eventDetect as ed
import mirlib.FFTParams as fftparams
from matplotlib.pylab import *


#inputfile = '../audio_files/GV02_A_Format4min.wav'
inputfile = '../audio_files/wburgShort.wav'

if not os.path.exists(inputfile):
    raise Exception("FILE DOES NOT EXIST, TRY AGAIN")

[x, fs] = M.wavread(inputfile)

# FFT Parameters
N = 2048
hopDenom = 2
zp = 0
winfunc=np.hamming
fftParams = fftparams.FFTParams(fs, N, hopDenom, zp, winfunc)

#peaks = z.envelopeFollowEnergy(winLen,hopSize) # the old way
z = ed.onsetDetect(fftParams)


events = z.findEventLocations(x)

xConcat = x[(events)]

示例#3

文件： mir_utils.py 项目： cjacoby/mir-noise

def ReadWavFile(filename):
    ''' Read wav data from the file at filename. '''
    return M.wavread(filename)

示例#4

文件： sones_test.py 项目： cjacoby/mir-noise

from numpy import *
from matplotlib.pyplot import *
import marlib.matlab as M
import scipy as sp
import math

import mirlib.FFTParams as fftparams
import mirlib.feature_extraction.calcLoudness as cl


[x,fs] = M.wavread('../audio_files/WB_12-15_342pm10mins.wav')
winLen = 4096*8
hopSize = winLen
# FFT Parameters
N = 4096*8
hopDenom = 1.
hopSize = N/float(hopDenom)
zp = 0
winfunc=np.hamming
fftParams = fftparams.FFTParams(fs, N, hopDenom, zp, winfunc)



#[x,fs] = M.wavread('RZABR40.wav')
#[x,fs] = M.wavread('RZABR40pad.wav')

z = cl.SoneCalculator(x, fftParams)
sonVec = z.calcSoneLoudness()

close()

示例#5

import os
import numpy as np
import marlib.matlab as M
import mirlib.feature_extraction.eventDetect as ed
import mirlib.FFTParams as fftparams
from matplotlib.pylab import *

#inputfile = '../audio_files/GV02_A_Format4min.wav'
inputfile = '../audio_files/wburgShort.wav'

if not os.path.exists(inputfile):
    raise Exception("FILE DOES NOT EXIST, TRY AGAIN")

[x, fs] = M.wavread(inputfile)

# FFT Parameters
N = 2048
hopDenom = 2
zp = 0
winfunc = np.hamming
fftParams = fftparams.FFTParams(fs, N, hopDenom, zp, winfunc)

#peaks = z.envelopeFollowEnergy(winLen,hopSize) # the old way
z = ed.onsetDetect(fftParams)

events = z.findEventLocations(x)

xConcat = x[(events)]

M.wavwrite(xConcat, "segs.wav", fs)

示例#6

from numpy import *
from matplotlib.pyplot import *
import marlib.matlab as M
import scipy as sp
import math

import mirlib.FFTParams as fftparams
import mirlib.feature_extraction.calcLoudness as cl

[x, fs] = M.wavread('../audio_files/WB_12-15_342pm10mins.wav')
winLen = 4096 * 8
hopSize = winLen
# FFT Parameters
N = 4096 * 8
hopDenom = 1.
hopSize = N / float(hopDenom)
zp = 0
winfunc = np.hamming
fftParams = fftparams.FFTParams(fs, N, hopDenom, zp, winfunc)

#[x,fs] = M.wavread('RZABR40.wav')
#[x,fs] = M.wavread('RZABR40pad.wav')

z = cl.SoneCalculator(x, fftParams)
sonVec = z.calcSoneLoudness()

close()

fig = figure()
ax1 = fig.add_subplot(111)
signalTime = arange(z.y.size)