def SpreadSignalEnergy(data, THR, freqs, sampleRate):
"""
Set SMR for each critical band in sfBands.
Arguments:
data: is an array of N time domain samples
MDCTdata: is an array of N/2 MDCT frequency lines for the sin windowed
in data which have been scaled up by a factor
of 2^MDCTscale
FFTdata is an array of N
frequency lines
sampleRate: is the sampling rate of the time domain samples
sfBands: points to information about which MDCT frequency lines
are in which scale factor band
Returns:
final= an array showing an SPL value of the masking curve for every line
Logic:
Performs an FFT of data[N] and identifies tonal and noise maskers.
Sums their masking curves with the hearing threshold at each MDCT
frequency location to the calculate absolute threshold at those
points. Then determines the maximum signal-to-mask ratio within
each critical band and returns that result in the SMR[] array.
"""
N=len(data)
Fs=sampleRate
windata=HanningWindow(data) #window with a Hanning window
xk=np.fft.fft(windata)
xksqr=np.square(np.abs(xk))
aintdft=float(4)/((N**2)*0.375)*xksqr
freqint= np.arange(0,Fs,float(Fs)/(float(N)))
frontfreq=freqint[0:N/2]
r=spk.findpeaks(aintdft)
fronthalfdft=aintdft[0:N/2]
freqidx=r[0]
freqidxfloats=freqidx
freqidx=np.array([(lambda x: int(x))(ii) for ii in freqidx]) #converts into integers
peakintensities=r[1]
freqidxhalf=freqidx[0:len(freqidx)/2] #peak frequency indices
peakstrint=peakintensities[0:len(peakintensities)/2]#peak intensity values
spldft=SPL(peakstrint)
setzeros=np.zeros(N/2)
freqloca=setzeros
actualfreq=float(Fs)*(freqidxhalf)/N+ float(Fs)/N*0.5
#create a mask for each frequency found
maskerarray=np.array([])
for ii in range(len(spldft)):
m=SpreadEnergy(actualfreq[ii],spldft[ii],True)
maskerarray=np.append(maskerarray,m)
fin=Thresh(freqs)
quietthresintens=Intensity(fin)
zvec=Bark(freqs)
sumofmasks=np.array(quietthresintens) #masks are added to threshold of quiet
for ii in range(len(maskerarray)): #addition of all the masks
tempmask=maskerarray[ii]
sumofmasks=sumofmasks+np.power(tempmask.vIntensityAtBark(zvec),SCALEA)
sumofmasks = np.power(sumofmasks,1./SCALEA)
maskthres=SPL(sumofmasks)
return maskthres # TO REPLACE WITH YOUR CODE
def CalcSMRs(data, MDCTdata, MDCTscale, sampleRate, sfBands):
"""
Set SMR for each critical band in sfBands.
Arguments:
data: is an array of N time domain samples
MDCTdata: is an array of N/2 MDCT frequency lines for the data
in data which have been scaled up by a factor
of 2^MDCTscale
MDCTscale: is an overall scale factor for the set of MDCT
frequency lines
sampleRate: is the sampling rate of the time domain samples
sfBands: points to information about which MDCT frequency lines
are in which scale factor band
Returns:
SMR[sfBands.nBands] is the maximum signal-to-mask ratio in each
scale factor band
Logic:
Performs an FFT of data[N] and identifies tonal and noise maskers.
Sums their masking curves with the hearing threshold at each MDCT
frequency location to the calculate absolute threshold at those
points. Then determines the maximum signal-to-mask ratio within
each critical band and returns that result in the SMR[] array.
"""
N=len(data)
Fs=sampleRate
windata=HanningWindow(data) #window with a Hanning window
xk=np.fft.fft(windata)
xksqr=np.square(np.abs(xk))
aintdft=float(4)/((N**2)*0.375)*xksqr
freqint= np.arange(0,Fs,float(Fs)/(float(N)))
frontfreq = np.float_((np.arange(len(MDCTdata))+.5)*Fs)/np.float(len(MDCTdata)*2)
r=spk.findpeaks(aintdft)
fronthalfdft=aintdft[0:N/2]
freqidx=r[0]
freqidxfloats=freqidx
freqidx=np.array([(lambda x: int(x))(ii) for ii in freqidx]) #converts into integers
peakintensities=r[1]
freqidxhalf=freqidx[0:len(freqidx)/2] #peak frequency indices
peakstrint=peakintensities[0:len(peakintensities)/2]#peak intensity values
spldft=SPL(peakstrint)
setzeros=np.zeros(N/2)
freqloca=setzeros
actualfreq=float(Fs)*(freqidxhalf)/N+ float(Fs)/N*0.5
#create a mask for each frequency found
maskerarray=np.array([])
for ii in range(len(spldft)):
m=Masker(actualfreq[ii],spldft[ii],True)
maskerarray=np.append(maskerarray,m)
fin=Thresh(frontfreq)
quietthresintens=Intensity(fin)
zvec=Bark(frontfreq)
sumofmasks=np.array(quietthresintens) #masks are added to threshold of quiet
for ii in range(len(maskerarray)): #addition of all the masks
tempmask=maskerarray[ii]
sumofmasks=sumofmasks+np.power(tempmask.vIntensityAtBark(zvec),SCALEA)
sumofmasks = np.power(sumofmasks,1./SCALEA)
maskthres=SPL(sumofmasks)
#####do MDCT lines correspond to half frequency away?
MDCTdatanorm=MDCTdata/float(2**MDCTscale)
MDCTintensity= 4*np.square(MDCTdatanorm)
SMR=SPL(MDCTintensity)-maskthres
llim=sfBands.lowerLine
ulim=sfBands.upperLine
numBands=sfBands.nBands
SMRband=np.zeros(numBands)
for ii in range(sfBands.nBands):
idxlower=llim[ii]
idxhigh=ulim[ii]
SMRband[ii]=np.max(SMR[idxlower:idxhigh+1])
return SMRband # TO REPLACE WITH YOUR CODE
