本文整理汇总了Python中utilFunctions.peakDetection函数的典型用法代码示例。如果您正苦于以下问题：Python peakDetection函数的具体用法？Python peakDetection怎么用？Python peakDetection使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了peakDetection函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: f0Twm
def f0Twm(x, fs, w, N, H, t, minf0, maxf0, f0et):
# fundamental frequency detection using twm algorithm
# x: input sound, fs: sampling rate, w: analysis window,
# N: FFT size (minimum 512), t: threshold in negative dB,
# minf0: minimum f0 frequency in Hz, maxf0: maximim f0 frequency in Hz,
# f0et: error threshold in the f0 detection (ex: 5),
# returns f0: fundamental frequency
hN = N/2 # size of positive spectrum
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
x = np.append(np.zeros(hM2),x) # add zeros at beginning to center first window at sample 0
x = np.append(x,np.zeros(hM1)) # add zeros at the end to analyze last sample
pin = hM1 # init sound pointer in middle of anal window
pend = x.size - hM1 # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
w = w / sum(w) # normalize analysis window
f0 = []
f0t = 0
f0stable = 0
while pin<pend:
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, hN, t) # detect peak locations
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values
ipfreq = fs * iploc/N
f0t = UF.f0DetectionTwm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) # find f0
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t # consider a stable f0 if it is close to the previous one
else:
f0stable = 0
f0 = np.append(f0, f0t)
pin += H # advance sound pointer
return f0
开发者ID:Jose-Coursera，项目名称:sms-tools，代码行数:35，代码来源:harmonicModel.py
示例2: check_k
def check_k(fr1, fr2, fs, k, window):
fr = fr1
for fr in fr1 + np.arange(fr2-fr1):
t = np.arange(441000.0)
x = np.sin(2.0*np.pi * fr * t / fs)
M = 100*k+1
i=0
while (2**i) < M:
i+=1
N = 2**i
h = M/2
l_h = len(x)/2 - h + 1
h_h = l_h + M
x_cnk = x[l_h:h_h]
w = get_window(window, M)
(mX, pX) = DFT.dftAnal(x_cnk, w, N)
p_loc = UF.peakDetection(mX, -40)
p_int = UF.peakInterp(mX, pX, p_loc)
peak = p_int[0]*(fs/float(N))
p = peak[0]
if abs(p-fr) > 0.05:
print "fr: ", fr, " error: ", abs(p-fr)
return 0
print "fr: ", fr, " checked"
fr+=1
return 3
开发者ID:akkeh，项目名称:SOGM_jr3，代码行数:28，代码来源:A5Part1.py
示例3: minFreqEstErr
def minFreqEstErr(inputFile, f):
"""
Inputs:
inputFile (string) = wav file including the path
f (float) = frequency of the sinusoid present in the input audio signal (Hz)
Output:
fEst (float) = Estimated frequency of the sinusoid (Hz)
M (int) = Window size
N (int) = FFT size
"""
# analysis parameters:
window = 'blackman'
t = -40
fs, x = UF.wavread(inputFile)
x_half = len(x) // 2
f_error = np.inf
k = 1
while f_error > 0.05: # Hz
M = 100 * k + 1
M2 = M // 2
W = get_window(window, M)
N = int(2 ** np.ceil(np.log2(M)))
mX, pX = DFT.dftAnal(x[x_half - M2: x_half - M2 + M], W, N)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
fEst = iploc * fs / N
f_error = np.abs(f - fEst)
k += 1
return(fEst, M, N)
开发者ID:Jee-Bee，项目名称:ASPFMA，代码行数:29，代码来源:A5Part1.py
示例4: harmonicModel
def harmonicModel(x, fs, w, N, t, nH, minf0, maxf0, f0et):
"""
Analysis/synthesis of a sound using the sinusoidal harmonic model
x: input sound, fs: sampling rate, w: analysis window,
N: FFT size (minimum 512), t: threshold in negative dB,
nH: maximum number of harmonics, minf0: minimum f0 frequency in Hz,
maxf0: maximim f0 frequency in Hz,
f0et: error threshold in the f0 detection (ex: 5),
returns y: output array sound
"""
hN = N/2 # size of positive spectrum
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
x = np.append(np.zeros(hM2),x) # add zeros at beginning to center first window at sample 0
x = np.append(x,np.zeros(hM1)) # add zeros at the end to analyze last sample
Ns = 512 # FFT size for synthesis (even)
H = Ns/4 # Hop size used for analysis and synthesis
hNs = Ns/2
pin = max(hNs, hM1) # init sound pointer in middle of anal window
pend = x.size - max(hNs, hM1) # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
yh = np.zeros(Ns) # initialize output sound frame
y = np.zeros(x.size) # initialize output array
w = w / sum(w) # normalize analysis window
sw = np.zeros(Ns) # initialize synthesis window
ow = triang(2*H) # overlapping window
sw[hNs-H:hNs+H] = ow
bh = blackmanharris(Ns) # synthesis window
bh = bh / sum(bh) # normalize synthesis window
sw[hNs-H:hNs+H] = sw[hNs-H:hNs+H] / bh[hNs-H:hNs+H] # window for overlap-add
hfreqp = []
f0t = 0
f0stable = 0
while pin<pend:
#-----analysis-----
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect peak locations
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values
ipfreq = fs * iploc/N
f0t = UF.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) # find f0
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t # consider a stable f0 if it is close to the previous one
else:
f0stable = 0
hfreq, hmag, hphase = harmonicDetection(ipfreq, ipmag, ipphase, f0t, nH, hfreqp, fs) # find harmonics
hfreqp = hfreq
#-----synthesis-----
Yh = UF.genSpecSines(hfreq, hmag, hphase, Ns, fs) # generate spec sines
fftbuffer = np.real(ifft(Yh)) # inverse FFT
yh[:hNs-1] = fftbuffer[hNs+1:] # undo zero-phase window
yh[hNs-1:] = fftbuffer[:hNs+1]
y[pin-hNs:pin+hNs] += sw*yh # overlap-add
pin += H # advance sound pointer
y = np.delete(y, range(hM2)) # delete half of first window which was added in stftAnal
y = np.delete(y, range(y.size-hM1, y.size)) # add zeros at the end to analyze last sample
return y
开发者ID:ronggong，项目名称:JingjuAriasMIRAnalysis，代码行数:59，代码来源:harmonicModel.py
示例5: sprModel
def sprModel(x, fs, w, N, t):
"""
Analysis/synthesis of a sound using the sinusoidal plus residual model, one frame at a time
x: input sound, fs: sampling rate, w: analysis window,
N: FFT size (minimum 512), t: threshold in negative dB,
returns y: output sound, ys: sinusoidal component, xr: residual component
"""
hN = N/2 # size of positive spectrum
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
Ns = 512 # FFT size for synthesis (even)
H = Ns/4 # Hop size used for analysis and synthesis
hNs = Ns/2
pin = max(hNs, hM1) # initialize sound pointer in middle of analysis window
pend = x.size - max(hNs, hM1) # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
ysw = np.zeros(Ns) # initialize output sound frame
xrw = np.zeros(Ns) # initialize output sound frame
ys = np.zeros(x.size) # initialize output array
xr = np.zeros(x.size) # initialize output array
w = w / sum(w) # normalize analysis window
sw = np.zeros(Ns)
ow = triang(2*H) # overlapping window
sw[hNs-H:hNs+H] = ow
bh = blackmanharris(Ns) # synthesis window
bh = bh / sum(bh) # normalize synthesis window
wr = bh # window for residual
sw[hNs-H:hNs+H] = sw[hNs-H:hNs+H] / bh[hNs-H:hNs+H]
while pin<pend:
#-----analysis-----
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # find peaks
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values
ipfreq = fs*iploc/float(N) # convert peak locations to Hertz
ri = pin-hNs-1 # input sound pointer for residual analysis
xw2 = x[ri:ri+Ns]*wr # window the input sound
fftbuffer = np.zeros(Ns) # reset buffer
fftbuffer[:hNs] = xw2[hNs:] # zero-phase window in fftbuffer
fftbuffer[hNs:] = xw2[:hNs]
X2 = fft(fftbuffer) # compute FFT for residual analysis
#-----synthesis-----
Ys = UF.genSpecSines(ipfreq, ipmag, ipphase, Ns, fs) # generate spec of sinusoidal component
Xr = X2-Ys; # get the residual complex spectrum
fftbuffer = np.zeros(Ns)
fftbuffer = np.real(ifft(Ys)) # inverse FFT of sinusoidal spectrum
ysw[:hNs-1] = fftbuffer[hNs+1:] # undo zero-phase window
ysw[hNs-1:] = fftbuffer[:hNs+1]
fftbuffer = np.zeros(Ns)
fftbuffer = np.real(ifft(Xr)) # inverse FFT of residual spectrum
xrw[:hNs-1] = fftbuffer[hNs+1:] # undo zero-phase window
xrw[hNs-1:] = fftbuffer[:hNs+1]
ys[ri:ri+Ns] += sw*ysw # overlap-add for sines
xr[ri:ri+Ns] += sw*xrw # overlap-add for residual
pin += H # advance sound pointer
y = ys+xr # sum of sinusoidal and residual components
return y, ys, xr
开发者ID:2opremio，项目名称:sms-tools，代码行数:58，代码来源:sprModel.py
示例6: peak
def peak(m, n):
hfs = fs * 0.5
x1 = x[hfs-m/2:hfs+(m+1)/2]
w = get_window(window, m)
mX, pX = DFT.dftAnal(x1, w, n)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
fest = fs * iploc[0] / n
return fest, ploc, mX, pX
开发者ID:icoxfog417，项目名称:sms-tools-workspace，代码行数:10，代码来源:A5Part1.py
示例7: run_one_estimate
def run_one_estimate(x, fs, M, window=DEFAULT_WINDOW, t=DEFAULT_THRESHOLD):
center_sample = int(len(x) / 2)
start_sample = center_sample - int(M / 2)
end_sample = start_sample + M
N = min_power_2(M)
x1 = x[start_sample:end_sample]
w = get_window(window, M)
mX, pX = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
fEst = iploc * fs / N
return (mX, pX, ploc, iploc, ipmag, ipphase, fEst, N)
开发者ID:pearpan，项目名称:asp-class，代码行数:12，代码来源:A5Part1.py
示例8: harmonicModelAnal
def harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope=0.01, minSineDur=0.02):
"""
Analysis of a sound using the sinusoidal harmonic model
x: input sound; fs: sampling rate, w: analysis window; N: FFT size (minimum 512); t: threshold in negative dB,
nH: maximum number of harmonics; minf0: minimum f0 frequency in Hz,
maxf0: maximim f0 frequency in Hz; f0et: error threshold in the f0 detection (ex: 5),
harmDevSlope: slope of harmonic deviation; minSineDur: minimum length of harmonics
returns xhfreq, xhmag, xhphase: harmonic frequencies, magnitudes and phases
"""
if minSineDur < 0: # raise exception if minSineDur is smaller than 0
raise ValueError("Minimum duration of sine tracks smaller than 0")
hN = N / 2 # size of positive spectrum
hM1 = int(math.floor((w.size + 1) / 2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size / 2)) # half analysis window size by floor
x = np.append(np.zeros(hM2), x) # add zeros at beginning to center first window at sample 0
x = np.append(x, np.zeros(hM2)) # add zeros at the end to analyze last sample
pin = hM1 # init sound pointer in middle of anal window
pend = x.size - hM1 # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
w = w / sum(w) # normalize analysis window
hfreqp = [] # initialize harmonic frequencies of previous frame
f0t = 0 # initialize f0 track
f0stable = 0 # initialize f0 stable
while pin <= pend:
x1 = x[pin - hM1 : pin + hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect peak locations
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values
ipfreq = fs * iploc / N # convert locations to Hz
f0t = UF.f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) # find f0
if ((f0stable == 0) & (f0t > 0)) or ((f0stable > 0) & (np.abs(f0stable - f0t) < f0stable / 5.0)):
f0stable = f0t # consider a stable f0 if it is close to the previous one
else:
f0stable = 0
hfreq, hmag, hphase = harmonicDetection(
ipfreq, ipmag, ipphase, f0t, nH, hfreqp, fs, harmDevSlope
) # find harmonics
hfreqp = hfreq
if pin == hM1: # first frame
xhfreq = np.array([hfreq])
xhmag = np.array([hmag])
xhphase = np.array([hphase])
else: # next frames
xhfreq = np.vstack((xhfreq, np.array([hfreq])))
xhmag = np.vstack((xhmag, np.array([hmag])))
xhphase = np.vstack((xhphase, np.array([hphase])))
pin += H # advance sound pointer
xhfreq = SM.cleaningSineTracks(xhfreq, round(fs * minSineDur / H)) # delete tracks shorter than minSineDur
return xhfreq, xhmag, xhphase
开发者ID:ChunHungLiu，项目名称:sms-tools，代码行数:51，代码来源:harmonicModel.py
示例9: minFreqEstErr
def minFreqEstErr(inputFile, f):
"""
Inputs:
inputFile (string) = wav file including the path
f (float) = frequency of the sinusoid present in the input audio signal (Hz)
Output:
fEst (float) = Estimated frequency of the sinusoid (Hz)
M (int) = Window size
N (int) = FFT size
"""
# analysis parameters:
window = 'blackman'
t = -40
#read the file
fs, s = UF.wavread(inputFile)
fEst = 0
error = abs(f - fEst)
k = 1
#begin iteration
while error > 0.05:
#set window_size for this iteration
M = 100 * k + 1
#compute FFT size as next power of two
exponent = int(np.log2(M)) + 1
FFT_size = 2**exponent
df = float(fs) / FFT_size
#slice the input signal
s_sliced = s[0.5 * fs - M/2 : 0.5 * fs + M/2 + 1]
#generate window
w = get_window("blackman", M)
#compute DFT
mX, pX = DFT.dftAnal(s_sliced, w, FFT_size)
#detect the peaks
peak_locations = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, peak_locations)
fEst = iploc[0] * df
error = abs(fEst - f)
k += 1
return (fEst, M, FFT_size)
开发者ID:phenylalaninqualle，项目名称:aspma_exchange，代码行数:51，代码来源:A5Part1.py
示例10: sineModelAnal
def sineModelAnal(x, fs, w, N, H, t, maxnSines = 100, minSineDur=.01, freqDevOffset=20, freqDevSlope=0.01):
"""
Analysis of a sound using the sinusoidal model with sine tracking
x: input array sound, w: analysis window, N: size of complex spectrum, H: hop-size, t: threshold in negative dB
maxnSines: maximum number of sines per frame, minSineDur: minimum duration of sines in seconds
freqDevOffset: minimum frequency deviation at 0Hz, freqDevSlope: slope increase of minimum frequency deviation
returns xtfreq, xtmag, xtphase: frequencies, magnitudes and phases of sinusoidal tracks
"""
if (minSineDur <0): # raise error if minSineDur is smaller than 0
raise ValueError("Minimum duration of sine tracks smaller than 0")
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
x = np.append(np.zeros(hM2),x) # add zeros at beginning to center first window at sample 0
x = np.append(x,np.zeros(hM2)) # add zeros at the end to analyze last sample
pin = hM1 # initialize sound pointer in middle of analysis window
pend = x.size - hM1 # last sample to start a frame
w = w / sum(w) # normalize analysis window
tfreq = np.array([])
while pin<pend: # while input sound pointer is within sound
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect locations of peaks
pmag = mX[ploc] # get the magnitude of the peaks
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values by interpolation
ipfreq = fs*iploc/float(N) # convert peak locations to Hertz
# perform sinusoidal tracking by adding peaks to trajectories
tfreq, tmag, tphase = sineTracking(ipfreq, ipmag, ipphase, tfreq, freqDevOffset, freqDevSlope)
tfreq = np.resize(tfreq, min(maxnSines, tfreq.size)) # limit number of tracks to maxnSines
tmag = np.resize(tmag, min(maxnSines, tmag.size)) # limit number of tracks to maxnSines
tphase = np.resize(tphase, min(maxnSines, tphase.size)) # limit number of tracks to maxnSines
jtfreq = np.zeros(maxnSines) # temporary output array
jtmag = np.zeros(maxnSines) # temporary output array
jtphase = np.zeros(maxnSines) # temporary output array
jtfreq[:tfreq.size]=tfreq # save track frequencies to temporary array
jtmag[:tmag.size]=tmag # save track magnitudes to temporary array
jtphase[:tphase.size]=tphase # save track magnitudes to temporary array
if pin == hM1: # if first frame initialize output sine tracks
xtfreq = jtfreq
xtmag = jtmag
xtphase = jtphase
else: # rest of frames append values to sine tracks
xtfreq = np.vstack((xtfreq, jtfreq))
xtmag = np.vstack((xtmag, jtmag))
xtphase = np.vstack((xtphase, jtphase))
pin += H
# delete sine tracks shorter than minSineDur
xtfreq = cleaningSineTracks(xtfreq, round(fs*minSineDur/H))
return xtfreq, xtmag, xtphase
开发者ID:hello-sergei，项目名称:sms-tools，代码行数:50，代码来源:sineModel.py
示例11: sineModelMultiRes
def sineModelMultiRes(x, fs, wList, NList, t, BList):
"""
Analysis/synthesis of a sound using the sinusoidal model, without sine tracking
x: input array sound, w: analysis window, N: size of complex spectrum, t: threshold in negative dB
returns y: output array sound
"""
#-----synthesis params init-----
Ns = 512 # FFT size for synthesis (even)
H = Ns/4 # Hop size used for analysis and synthesis
hNs = Ns/2 # half of synthesis FFT size
yw = np.zeros(Ns) # initialize output sound frame
y = np.zeros(x.size) # initialize output array
sw = np.zeros(Ns) # initialize synthesis window
ow = triang(2*H) # triangular window
sw[hNs-H:hNs+H] = ow # add triangular window
bh = blackmanharris(Ns) # blackmanharris window
bh = bh / sum(bh) # normalized blackmanharris window
sw[hNs-H:hNs+H] = sw[hNs-H:hNs+H] / bh[hNs-H:hNs+H] # normalized synthesis window
for i in range(3):
#-----analysis params init-----
w = wList[i]
N = NList[i]
Bmin = BList[i][0]
Bmax = BList[i][1]
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
pin = max(hNs, hM1) # init sound pointer in middle of anal window
pend = x.size - max(hNs, hM1) # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
w = w / sum(w) # normalize analysis window
while pin<pend: # while input sound pointer is within sound
#-----analysis-----
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect locations of peaks
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values by interpolation
ipfreq = fs*iploc/float(N) # convert peak locations to Hertz
ipmag = ipmag[np.logical_and(ipfreq>=Bmin, ipfreq<Bmax)]
ipphase = ipphase[np.logical_and(ipfreq>=Bmin, ipfreq<Bmax)]
ipfreq = ipfreq[np.logical_and(ipfreq>=Bmin, ipfreq<Bmax)]
#-----synthesis-----
Y = UF.genSpecSines(ipfreq, ipmag, ipphase, Ns, fs) # generate sines in the spectrum
fftbuffer = np.real(ifft(Y)) # compute inverse FFT
yw[:hNs-1] = fftbuffer[hNs+1:] # undo zero-phase window
yw[hNs-1:] = fftbuffer[:hNs+1]
y[pin-hNs:pin+hNs] += sw*yw # overlap-add and apply a synthesis window
pin += H # advance sound pointer
return y
开发者ID:dshoot，项目名称:sms-tools，代码行数:50，代码来源:sineModel.py
示例12: minFreqEstErr
def minFreqEstErr(inputFile, f):
"""
Inputs:
inputFile (string) = wav file including the path
f (float) = frequency of the sinusoid present in the input audio signal (Hz)
Output:
fEst (float) = Estimated frequency of the sinusoid (Hz)
M (int) = Window size
N (int) = FFT size
"""
# analysis parameters:
window = 'blackman'
t = -40
### Your code here
k = 1
M = 100*k + 1
N = nextPow2(M)
fs, x = UF.wavread(inputFile)
w = get_window(window, M)
x1 = x[ 0.5*fs - M/2.0 : 0.5*fs + M/2.0]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
fEst = iploc * fs / N
while len(fEst) < 1 or abs(f - fEst[0]) >= 0.05:
k += 1
M = 100*k + 1
N = nextPow2(M)
w = get_window(window, M)
fs, x = UF.wavread(inputFile)
x1 = x[ 0.5*fs - M/2.0 : 0.5*fs + M/2.0]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX, t)
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc)
fEst = iploc * fs / N
return (fEst[0], M, N)
开发者ID:feynmanliang，项目名称:sms-tools，代码行数:37，代码来源:A5Part1.py
示例13: sineModelMultiRes
def sineModelMultiRes(x, fs, w, N, t, B):
"""
Analysis/synthesis of a sound using the sinusoidal model, without sine tracking
x: input array sound, w: array of analysis windows, N: array of sizes of complex spectrum,
t: threshold in negative dB, B: array of frequency bands
returns y: output array sound
"""
hM1 = [int(math.floor((_w.size + 1) / 2)) for _w in w] # half analysis window(s) size by rounding
hM2 = [int(math.floor(_w.size / 2)) for _w in w] # half analysis window(s) size by floor
Ns = 512 # FFT size for synthesis (even)
H = Ns / 4 # Hop size used for analysis and synthesis
hNs = Ns / 2 # half of synthesis FFT size
pin = max(hNs, max(hM1)) # init sound pointer in middle of anal window
pend = x.size - max(hNs, max(hM1)) # last sample to start a frame
fftbuffer = np.array([]) # initialize buffer for FFT
yw = np.zeros(Ns) # initialize output sound frame
y = np.zeros(x.size) # initialize output array
w = [_w / sum(_w) for _w in w] # normalize analysis window(s)
sw = np.zeros(Ns) # initialize synthesis window
ow = triang(2 * H) # triangular window
sw[hNs - H : hNs + H] = ow # add triangular window
bh = blackmanharris(Ns) # blackmanharris window
bh = bh / sum(bh) # normalized blackmanharris window
sw[hNs - H : hNs + H] = sw[hNs - H : hNs + H] / bh[hNs - H : hNs + H] # normalized synthesis window
while pin < pend: # while input sound pointer is within sound
# -----analysis-----
ipmag = ipphase = ipfreq = np.array([]) # initialize the synthesis arrays
for i in range(0, len(w)): # for each window, use some loop variables ('_' prefix)
_hM1, _hM2, _w, _N, _B = (hM1[i], hM2[i], w[i], N[i], B[i])
x1 = x[pin - _hM1 : pin + _hM2] # select frame
mX, pX = DFT.dftAnal(x1, _w, _N) # compute dft
ploc = UF.peakDetection(mX, t) # detect locations of peaks
iploc, _ipmag, _ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values by interpolation
_ipfreq = fs * iploc / float(_N) # convert peak locations to Hertz
lo, hi = (_B[0], _B[1]) # low/high from band tuples [..(lo, hi)..]
mask = (_ipfreq >= lo) * (_ipfreq < hi) # mask for in-band components
ipmag = np.append(ipmag, _ipmag * mask) # mask and append components
ipphase = np.append(ipphase, _ipphase * mask)
ipfreq = np.append(ipfreq, _ipfreq * mask)
# -----synthesis-----
Y = UF.genSpecSines(ipfreq, ipmag, ipphase, Ns, fs) # generate sines in the spectrum
fftbuffer = np.real(ifft(Y)) # compute inverse FFT
yw[: hNs - 1] = fftbuffer[hNs + 1 :] # undo zero-phase window
yw[hNs - 1 :] = fftbuffer[: hNs + 1]
y[pin - hNs : pin + hNs] += sw * yw # overlap-add and apply a synthesis window
pin += H # advance sound pointer
return y
开发者ID:raffaelenoro，项目名称:sms-tools，代码行数:48，代码来源:sineModel.py
示例14: test
def test():
window = 'blackman'
t = -40
fs = 44100
a = [101, 200, 440]
k = 1
matched = False
while True:
M = (100*k) + 1
N = int(pow(2, np.ceil(np.log2(M))))
w = get_window(window, M)
for f in np.arange(100,8000):
#for i in range(len(a)):
#f = a[i]
x = generateSine(f)
hx = len(x) / 2
x1 = x[(.5*fs)-(M/2):(.5*fs)+((M/2)+1)]
#x1 = x[hx-(M/2):hx+(M/2)+1]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX, t)
pmag = mX[ploc]
(iploc, ipmag, ipphase) = UF.peakInterp(mX, pX, ploc)
fEst = (fs * float(np.sum(iploc))) / float(N)
esterror = np.abs(fEst - f)
print esterror
if (esterror > 0.05):
matched = False
break
else:
matched = True
if matched:
break
else:
k += 1
print fEst
print k
print M
print N
开发者ID:yashiro32，项目名称:audio_signal_processing，代码行数:48，代码来源:get_window_size.py
示例15: sineModelAnal
def sineModelAnal(x, fs, w, N, H, t, maxnSines = 100, minSineDur=.01, freqDevOffset=20, freqDevSlope=0.01):
# Analysis of a sound using the sinusoidal model
# x: input array sound, w: analysis window, N: size of complex spectrum,
# H: hop-size, t: threshold in negative dB
# maxnSines: maximum number of sines per frame
# minSineDur: minimum duration of sines in seconds
# freqDevOffset: minimum frequency deviation at 0Hz
# freqDevSlope: slope increase of minimum frequency deviation
# returns xtfreq, xtmag, xtphase: frequencies, magnitudes and phases of sinusoids
hN = N/2 # size of positive spectrum
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
x = np.append(np.zeros(hM2),x) # add zeros at beginning to center first window at sample 0
x = np.append(x,np.zeros(hM2)) # add zeros at the end to analyze last sample
pin = hM1 # initialize sound pointer in middle of analysis window
pend = x.size - hM1 # last sample to start a frame
w = w / sum(w) # normalize analysis window
tfreq = np.array([])
while pin<pend: # while input sound pointer is within sound
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, hN, t) # detect locations of peaks
pmag = mX[ploc] # get the magnitude of the peaks
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values by interpolation
ipfreq = fs*iploc/float(N)
tfreq, tmag, tphase = UF.sineTracking(ipfreq, ipmag, ipphase, tfreq, freqDevOffset, freqDevSlope)
tfreq = np.resize(tfreq, min(maxnSines, tfreq.size))
tmag = np.resize(tmag, min(maxnSines, tmag.size))
tphase = np.resize(tphase, min(maxnSines, tphase.size))
jtfreq = np.zeros(maxnSines)
jtmag = np.zeros(maxnSines)
jtphase = np.zeros(maxnSines)
jtfreq[:tfreq.size]=tfreq
jtmag[:tmag.size]=tmag
jtphase[:tphase.size]=tphase
if pin == hM1:
xtfreq = jtfreq
xtmag = jtmag
xtphase = jtphase
else:
xtfreq = np.vstack((xtfreq, jtfreq))
xtmag = np.vstack((xtmag, jtmag))
xtphase = np.vstack((xtphase, jtphase))
pin += H
xtfreq = UF.cleaningSineTracks(xtfreq, round(fs*minSineDur/H))
return xtfreq, xtmag, xtphase
开发者ID:john36590，项目名称:sms-tools，代码行数:46，代码来源:sineModel.py
示例16: f0Detection
def f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et):
"""
Fundamental frequency detection of a sound using twm algorithm
x: input sound; fs: sampling rate; w: analysis window;
N: FFT size; t: threshold in negative dB,
minf0: minimum f0 frequency in Hz, maxf0: maximim f0 frequency in Hz,
f0et: error threshold in the f0 detection (ex: 5),
returns f0: fundamental frequency
"""
if (minf0 < 0): # raise exception if minf0 is smaller than 0
raise ValueError("Minumum fundamental frequency (minf0) smaller than 0")
if (maxf0 >= 10000): # raise exception if maxf0 is bigger than fs/2
raise ValueError("Maximum fundamental frequency (maxf0) bigger than 10000Hz")
if (H <= 0): # raise error if hop size 0 or negative
raise ValueError("Hop size (H) smaller or equal to 0")
hN = N/2 # size of positive spectrum
hM1 = int(math.floor((w.size+1)/2)) # half analysis window size by rounding
hM2 = int(math.floor(w.size/2)) # half analysis window size by floor
x = np.append(np.zeros(hM2),x) # add zeros at beginning to center first window at sample 0
x = np.append(x,np.zeros(hM1)) # add zeros at the end to analyze last sample
pin = hM1 # init sound pointer in middle of anal window
pend = x.size - hM1 # last sample to start a frame
fftbuffer = np.zeros(N) # initialize buffer for FFT
w = w / sum(w) # normalize analysis window
f0 = [] # initialize f0 output
f0t = 0 # initialize f0 track
f0stable = 0 # initialize f0 stable
while pin<pend:
x1 = x[pin-hM1:pin+hM2] # select frame
mX, pX = DFT.dftAnal(x1, w, N) # compute dft
ploc = UF.peakDetection(mX, t) # detect peak locations
iploc, ipmag, ipphase = UF.peakInterp(mX, pX, ploc) # refine peak values
ipfreq = fs * iploc/N # convert locations to Hez
f0t = f0Twm(ipfreq, ipmag, f0et, minf0, maxf0, f0stable) # find f0
if ((f0stable==0)&(f0t>0)) \
or ((f0stable>0)&(np.abs(f0stable-f0t)<f0stable/5.0)):
f0stable = f0t # consider a stable f0 if it is close to the previous one
else:
f0stable = 0
f0 = np.append(f0, f0t) # add f0 to output array
pin += H # advance sound pointer
return f0
开发者ID:Jee-Bee，项目名称:ASPFMA，代码行数:45，代码来源:A6Part4.py
示例17: minFreqEstErr
def minFreqEstErr(inputFile, f):
"""
Inputs:
inputFile (string) = wav file including the path
f (float) = frequency of the sinusoid present in the input audio signal (Hz)
Output:
fEst (float) = Estimated frequency of the sinusoid (Hz)
M (int) = Window size
N (int) = FFT size
"""
# analysis parameters:
window = 'blackman'
t = -40
### Your code here
(fs, x) = UF.wavread(inputFile)
numbins = 6
k = 21
#M = int(numbins * fs / f)
M = (100*k) + 1
N = int(pow(2, np.ceil(np.log2(M))))
w = get_window(window, M)
hx = len(x) / 2
x1 = x[(.5*fs)-(M/2):(.5*fs)+((M/2)+1)]
#x1 = x[hx-(M/2):hx+(M/2)+1]
mX, pX = DFT.dftAnal(x1, w, N)
ploc = UF.peakDetection(mX, t)
pmag = mX[ploc]
(iploc, ipmag, ipphase) = UF.peakInterp(mX, pX, ploc)
fEst = (fs * float(np.sum(iploc))) / float(N)
esterror = np.abs(fEst - f)
print esterror
return (fEst, M, N)
开发者ID:yashiro32，项目名称:audio_signal_processing，代码行数:44，代码来源:get_window_size.py
示例18: minFreqEstErr
def minFreqEstErr(inputFile, f):
"""
Inputs:
inputFile (string) = wav file including the path
f (float) = frequency of the sinusoid present in the input audio signal (Hz)
Output:
fEst (float) = Estimated frequency of the sinusoid (Hz)
M (int) = Window size
N (int) = FFT size
"""
# analysis parameters:
window = 'blackman'
t = -40
### Your code here
(fs, x) = UF.wavread(inputFile)
print fs
#k = find_k(100, 2000, fs, window)
k=1
while check_k(100, 2000, fs, k, window) < 2:
print k
k+=1
M = 100*k+1
print "M: ", M
i=0
while (2**i) < M:
i+=1
N = 2**i
print "N: ", N
print "fs: ", fs
print "length: ", len(x)
center = len(x)/2
h = M/2
x_cnk = x[center-h:center+h+1]
print "chunk: ", len(x_cnk)
w = get_window(window, M)
(mX, pX) = DFT.dftAnal(x_cnk, w, N)
p_loc = UF.peakDetection(mX, t)
p_int = UF.peakInterp(mX, pX, p_loc)
peak = p_int[0]*(fs/float(N))
return (peak[0], M, N)
开发者ID:akkeh，项目名称:SOGM_jr3，代码行数:43，代码来源:A5Part1.py
示例19: minFreqEstErr
def minFreqEstErr(inputFile, f):
"""
Inputs:
inputFile (string) = wav file including the path
f (float) = frequency of the sinusoid present in the input audio signal (Hz)
Output:
fEst (float) = Estimated frequency of the sinusoid (Hz)
M (int) = Window size
N (int) = FFT size
"""
# analysis parameters:
window = 'blackman'
t = -40
### Your code here
(fs, x) = UF.wavread(inputFile)
offset = 0.5
center = offset * fs
x_center = len(x) / 2
k = 2
while True:
M = 100 * k + 1
N = smallest_power_of_2_greater_than(M)
hM1 = int(math.floor((M+1)/2))
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