Пример #1
0
def estimate(inputFile='a7q2-harmonic.wav',
             window='blackman',
             M=2101,
             N=4096,
             t=-90,
             minSineDur=0.1,
             nH=50,
             minf0=100,
             maxf0=200,
             f0et=5,
             harmDevSlope=0.01):

    Ns = 512
    H = 128

    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                               maxf0, f0et, harmDevSlope,
                                               minSineDur)
    f0 = HM.f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et)
    y = SM.sineModelSynth(hfreq, hmag, hphase, Ns, H, fs)

    # plt.plot(x)
    # plt.plot(y)
    # plt.show()

    size = min([x.size, y.size])
    diff = np.sum(np.abs(x[:size] - y[:size]))
    std = np.std(f0)

    print "diff:{0} & std:{1}, M={2} N={3} t={4} minSineDur={5} nH={6} min/max={7}/{8} f0et={9} harmDevSlope={10}" \
    .format(diff, std, M, N, t, minSineDur, nH, minf0, maxf0, f0et, harmDevSlope)

    return diff, std
Пример #2
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    ### Your code here

    # 0. Read the audio file
    fs, x = UF.wavread(inputFile)

    # 1. Use harmonic model to to compute the harmonic frequencies and magnitudes
    w = get_window(window, M)
    harmDevSlope = 0.01
    minSineDur = 0.0
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                               maxf0, f0et, harmDevSlope,
                                               minSineDur)
    f0 = HM.f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et)

    # 2. Extract the segment in which you need to compute the inharmonicity.
    b1 = np.ceil(t1 * float(fs) / H)
    b2 = np.ceil(t2 * float(fs) / H)
    bhfreq = hfreq[b1:b2]
    bf0 = f0[b1:b2]

    # 3. Compute the mean inharmonicity for the segment
    inhm = np.array([])
    for idx, h in enumerate(bhfreq):
        coef = np.arange(1, h.size + 1)
        i = np.abs(h - coef * bf0[idx]) / coef
        inhm = np.append(inhm, np.sum(i) / len(i))

    return np.sum(inhm) / len(inhm)
Пример #3
0
def estimateInharmonicity(inputFile='piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """

    # Read the audio file
    (fs, x) = UF.wavread(inputFile)
    w = get_window(window, M)
    harmDevSlope = 0.01
    minSinDur = 0.0
    # Use harmonic model to to compute the harmonic frequencies and magnitudes
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                               maxf0, f0et, harmDevSlope,
                                               minSinDur)
    # Extract the segment in which you need to compute the inharmonicity.
    l1 = int(np.ceil(t1 * fs / H))
    l2 = int(np.ceil(t2 * fs / H))
    # Compute the mean inharmonicity for the segment
    Imean = 0
    d = np.array([])
    a = np.array([])
    frame = np.array([], ndmin=2)
    for i in range(l1, l2):
        R = nH
        I = 0
        for r in range(0, R):
            I += abs((hfreq[i][r] - (r + 1) * hfreq[i][0])) / (r + 1)
        I = I / R
        Imean += I
    Imean = Imean / (l2 - l1)

    return (Imean)
Пример #4
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    harmDevSlope = 0.01
    minSineDur = 0.0
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                                  maxf0, f0et, harmDevSlope,
                                                  minSineDur)
    f0 = HM.f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et)
    # 2. Extract the time segment in which you need to compute the inharmonicity.

    l1 = int(np.ceil(t1 * float(fs) / H))  #frame start
    l2 = int(np.ceil(t2 * float(fs) / H))  #frame end
    harmonicsFrame = xhfreq[l1:l2]
    f0Frame = f0[l1:l2]

    # 3. Compute the mean inharmonicity of the segment
    tempInhm = np.array([])
    for a, b in enumerate(harmonicsFrame):
        coefficient = np.arange(1, b.size + 1)
        inhP = np.abs(b - coefficient * f0Frame[a]) / coefficient
        tempInhm = np.append(tempInhm, np.sum(inhP) / len(inhP))
    meanInhm = np.sum(tempInhm) / len(tempInhm)
    return meanInhm
Пример #5
0
def estimateInharmonicity(inputFile = '../../sounds/piano.wav', t1=0.1, t2=0.5, window='hamming', 
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """    
    ### Your code here
    
    # 0. Read the audio file
    fs, x = UF.wavread(inputFile)                               #reading inputFile
    w  = get_window(window, M)                                  #obtaining analysis window 
    # 1. Use harmonic model to to compute the harmonic frequencies and magnitudes
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope = 0.01, minSineDur = 0.0)

    # xhfreq is a list of jagged arrays each array containing the list of harmonics in that frame 
    # 2. Extract the segment in which you need to compute the inharmonicity. 
    start = np.ceil(t1 * fs/H)
    end = np.floor(t2 * fs/H)
    hFreq = xhfreq[start:end+1]

    hMag = xhfreq[start:end+1]
    # 3. Compute the mean inharmonicity for the segment
    # NOTE that inharmonicity does nothing with the magnitude, it just looks at the frequency deviation/error
    # and HFreq[0] is the fundamental f0
    R = len(hFreq)
    print R
    print np.shape(hFreq)
    inharmonicity = []
    for frame in hFreq:
        #print frame
        inh =  map(lambda r: abs(frame[r - 1] - (r * frame[0])) / r, np.arange(1, len(frame) + 1))
        inharmonicity.append(np.sum(inh)/len(frame))
        print "Frame Inharmonicity = " + str(np.sum(inh)/len(frame))
    
    
    inharmonicity = np.sum(inharmonicity) / (end - start + 1)
    print "Total Inharmonicity = " + str(inharmonicity)
    return(inharmonicity)
Пример #6
0
def estimateInharmonicity(inputFile = '../../sounds/piano.wav', t1=0.1, t2=0.5, window='hamming', 
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """    
    ### Your code here
    
    # 0. Read the audio file
    fs, x = UF.wavread(inputFile)
    
    # 1. Use harmonic model to to compute the harmonic frequencies and magnitudes
    w  = get_window(window, M)
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(
        x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope=0.01, minSineDur=0.0)

    # 2. Extract the segment in which you need to compute the inharmonicity. 
    (nframes, nharm) = xhmag.shape
    secs_per_index = (x.size / fs / nframes)
    start_index = int(np.ceil(t1 / secs_per_index))
    end_index = int(np.floor(t2 / secs_per_index))

    # 3. Compute the mean inharmonicity for the segment
    I = np.zeros(nframes)
    for f in xrange(nframes):
        inharm = 0.0
        r = 0.0
        for h in xrange(1, nharm):
            if xhmag[f, h] != 0:
                r = (h+1)
                inharm += abs(xhfreq[f, h] - (h+1)*xhfreq[f, 0]) / (h+1)
        if r != 0:
            I[f] = inharm / r
    Imean = sum(I[i] for i in xrange(start_index, end_index+1))
    Imean /= (end_index - start_index + 1)

    return Imean
Пример #7
0
def hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf):
	# Analysis of a sound using the harmonic plus stochastic model
	# x: input sound, fs: sampling rate, w: analysis window, 
	# N: FFT size, 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: hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; mYst: stochastic residual
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
	mYst = UF.stochasticResidual(x, Ns, H, hfreq, hmag, hphase, fs, stocf)
	return hfreq, hmag, hphase, mYst
Пример #8
0
def estimateInharmonicity(inputFile = '../../sounds/piano.wav', t1=0.1, t2=0.5, window='hamming', 
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope=0.1, minSineDur=0.0)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    l1 = np.floor(t1 * fs / H)
    l2 = np.floor(t2 * fs / H)
    hfreq1 = hfreq[l1:l2+1]
    hmag1 = hmag[l1:l2+1]
    hphase1 = hphase[l1:l2+1]

    print(l1, l2)

    # 3. Compute the mean inharmonicity of the segment
    inharm = np.zeros(shape=(hfreq1.shape[0]))
    for l, freq in enumerate(hfreq1):
        R = freq.shape[0]
        f0 = freq[0]
        sum = 0.0
        for r in range(1, R + 1):
            if freq[r - 1] > 0.0:
                sum += np.abs(freq[r - 1] - r * f0) / r
        inharm[l] = sum / R

    meanInharm = np.sum(inharm) / (l2 - l1 + 1)
    return meanInharm
Пример #9
0
def main(inputFile='../../sounds/vignesh.wav',
         window='blackman',
         M=1201,
         N=2048,
         t=-90,
         minSineDur=0.1,
         nH=100,
         minf0=130,
         maxf0=300,
         f0et=7,
         harmDevSlope=0.01):
    """
	Analysis and synthesis using the harmonic model
	inputFile: input sound file (monophonic with sampling rate of 44100)
	window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)	
	M: analysis window size; N: fft size (power of two, bigger or equal than M)
	t: magnitude threshold of spectral peaks; minSineDur: minimum duration of sinusoidal tracks
	nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound
	maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm                                                                                            
	harmDevSlope: allowed deviation of harmonic tracks, higher harmonics could have higher allowed deviation
	"""

    # size of fft used in synthesis
    Ns = 512

    # hop size (has to be 1/4 of Ns)
    H = 128

    # read input sound
    (fs, x) = UF.wavread(inputFile)

    # compute analysis window
    w = get_window(window, M)

    # detect harmonics of input sound
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                               maxf0, f0et, harmDevSlope,
                                               minSineDur)

    # synthesize the harmonics
    y = SM.sineModelSynth(hfreq, hmag, hphase, Ns, H, fs)

    # output sound file (monophonic with sampling rate of 44100)
    outputFile = 'output_sounds/' + os.path.basename(
        inputFile)[:-4] + '_harmonicModel.wav'

    # write the sound resulting from harmonic analysis
    UF.wavwrite(y, fs, outputFile)
    return x, fs, hfreq, y
Пример #10
0
def hprModelAnal(x, fs, w, N, H, t, minSineDur, nH, minf0, maxf0, f0et, harmDevSlope):
	"""Analysis of a sound using the harmonic plus residual model
	x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, 
	minSineDur: minimum duration of sinusoidal tracks
	nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound
	maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm                                                                                            
	harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
	returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; xr: residual signal
	"""

	# perform harmonic analysis
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
	Ns = 512
	xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)    	# subtract sinusoids from original sound
	return hfreq, hmag, hphase, xr
Пример #11
0
def hprModelAnal(x, fs, w, N, H, t, minSineDur, nH, minf0, maxf0, f0et, harmDevSlope):
	"""Analysis of a sound using the harmonic plus residual model
	x: input sound, fs: sampling rate, w: analysis window; N: FFT size, t: threshold in negative dB, 
	minSineDur: minimum duration of sinusoidal tracks
	nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound
	maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm                                                                                            
	harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
	returns hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; xr: residual signal
	"""

	# perform harmonic analysis
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
	Ns = 512
	xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)    	# subtract sinusoids from original sound
	return hfreq, hmag, hphase, xr
Пример #12
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)  #reading inputFile
    w = get_window(window, M)  #obtaining analysis window
    f0 = HM.f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et)  #estimating F0

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    xhreq, xhmag, xhphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                                 maxf0, f0et)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    starting = int(np.ceil(fs * t1 / H))
    ending = int(np.floor(fs * t2 / H))

    # 3. Compute the mean inharmonicity of the segment
    mean_inharmonicity = compute_inharmonicity(xhreq, starting, ending, nH)

    return mean_inharmonicity
Пример #13
0
def estimateInharmonicity(inputFile = '../../sounds/piano.wav', t1=0.1, t2=0.5, window='hamming', 
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """    
    ### Your code here
    
    # 0. Read the audio file
    fs, x = UF.wavread(inputFile)
    
    # 1. Use harmonic model to to compute the harmonic frequencies and magnitudes
    w  = get_window(window, M)
    harmDevSlope=0.01
    minSineDur=0.0
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
    f0 = HM.f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et)
    
    # 2. Extract the segment in which you need to compute the inharmonicity. 
    b1 = np.ceil(t1 * float(fs)/H)
    b2 = np.ceil(t2 * float(fs)/H)
    bhfreq = hfreq[b1:b2]
    bf0 = f0[b1:b2]
    
    # 3. Compute the mean inharmonicity for the segment
    inhm = np.array([])
    for idx, h in enumerate(bhfreq):
        coef = np.arange(1, h.size+1)
        i = np.abs(h - coef * bf0[idx])/coef
        inhm = np.append(inhm, np.sum(i) / len(i))
    
    return np.sum(inhm) / len(inhm)
Пример #14
0
def estimateInharmonicity(
    inputFile="../../sounds/piano.wav",
    t1=0.1,
    t2=0.5,
    window="hamming",
    M=2048,
    N=2048,
    H=128,
    f0et=5.0,
    t=-90,
    minf0=130,
    maxf0=180,
    nH=10,
):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval
                                        t1 and t2.
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    xhfreq, _, _ = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope=0.01, minSineDur=0.0)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    xhfreq = xhfreq[np.ceil(fs * t1 / H) : np.ceil(fs * t2 / H), :]

    # 3. Compute the mean inharmonicity of the segment
    inh = map(inharmonicity, xhfreq)
    return np.mean(inh)
Пример #15
0
def estimateInharmonicity(inputFile = '../sms-tools/sounds/piano.wav', t1=0.1, t2=0.5, window='hamming',
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)
    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    # xhfreq, xhmag, xhphase = harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope=0.01, minSineDur=.02)
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    l1 = int(round((t1 * fs + 1 / 2 * H) / H, 0))  # estemated frame ate t1
    l2 = int(round((t2 * fs + 1 / 2 * H) / H, 0))  # estemated frame ate t2
    xhfreq = xhfreq[l1:l2]
    xhmag = xhmag[l1:l2]
    xhphase = xhphase[l1:l2]

    # 3. Compute the mean inharmonicity of the segment
    r = np.arange(1, nH + 1)
    R = nH
    #r = np.tile(r,(xhfreq.size,1))
    # fr = r*f0 sqrt(1 + Br2)
    I = []
    for Ival in range(xhfreq.shape[0]):
        temp = (np.abs(xhfreq[Ival] - r * xhfreq[Ival, 0]))
        I = np.append(I, 1 / R * np.sum(temp / r))
    meanInharm = 1 / (l2 - l1 + 1) * np.sum(I)
    return(meanInharm)
Пример #16
0
def hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf):
	"""
	Analysis of a sound using the harmonic plus stochastic model
	x: input sound, fs: sampling rate, w: analysis window; N: FFT size, 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 hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; stocEnv: stochastic residual
	"""

	# perform harmonic analysis
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
	# subtract sinusoids from original sound
	xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
	# perform stochastic analysis of residual    	
	stocEnv = STM.stochasticModelAnal(xr, H, H*2, stocf)
	return hfreq, hmag, hphase, stocEnv
Пример #17
0
def hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf):
	"""
	Analysis of a sound using the harmonic plus stochastic model
	x: input sound, fs: sampling rate, w: analysis window; N: FFT size, 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 hfreq, hmag, hphase: harmonic frequencies, magnitude and phases; stocEnv: stochastic residual
	"""

	# perform harmonic analysis
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
	# subtract sinusoids from original sound
	xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
	# perform stochastic analysis of residual    	
	stocEnv = STM.stochasticModelAnal(xr, H, H*2, stocf)
	return hfreq, hmag, hphase, stocEnv
Пример #18
0
def estimateInharmonicity(inputFile = '../../sounds/piano.wav', t1=0.1, t2=0.5, window='hamming', 
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """    
    ### Your code here
    
    # 0. Read the audio file
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)
    
    # 1. Use harmonic model to to compute the harmonic frequencies and magnitudes
    xhfreq, _, _ = HM.harmonicModelAnal(x, fs, w, N, \
        H, t, nH, minf0, maxf0, f0et, harmDevSlope = 0.01, minSineDur = 0.0)

    # 2. Extract the segment in which you need to compute the inharmonicity.
    f1 = int(np.ceil(t1 * fs / H))
    f2 = int(np.floor(t2 * fs / H))
    hfseg = xhfreq[f1:f2+1]

    # 3. Compute the mean inharmonicity for the segment
    inh = np.apply_along_axis(inharmonicity, 1, hfseg)
    imean = np.sum(inh) / (f2 - f1 + 1)
    
    return imean
Пример #19
0
def NoteHarmonic(FileName):

	# """
	# Analysis and synthesis using the harmonic model
	# inputFile: input sound file (monophonic with sampling rate of 44100)
	# window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
	# M: analysis window size; N: fft size (power of two, bigger or equal than M)
	# t: magnitude threshold of spectral peaks; minSineDur: minimum duration of sinusoidal tracks
	# nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound
	# maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm
	# harmDevSlope: allowed deviation of harmonic tracks, higher harmonics could have higher allowed deviation
	# """


	(fs, x) = UF.wavread(FileName)
	window='blackman'
	M=1201
	N=2048
	t=-90
	minSineDur=0.1
	nH=1
	minf0=75
	maxf0=500
	f0et=20
	harmDevSlope=0.01

	# size of fft used in synthesis
	Ns = 512

	# hop size (has to be 1/4 of Ns)
	H = 128

	# compute analysis window
	w = get_window(window, M)

	# detect harmonics of input sound
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

	return hfreq
Пример #20
0
def main(inputFile='../../sounds/vignesh.wav', window='blackman', M=1201, N=2048, t=-90, 
	minSineDur=0.1, nH=100, minf0=130, maxf0=300, f0et=7, harmDevSlope=0.01):
	"""
	Analysis and synthesis using the harmonic model
	inputFile: input sound file (monophonic with sampling rate of 44100)
	window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)	
	M: analysis window size; N: fft size (power of two, bigger or equal than M)
	t: magnitude threshold of spectral peaks; minSineDur: minimum duration of sinusoidal tracks
	nH: maximum number of harmonics; minf0: minimum fundamental frequency in sound
	maxf0: maximum fundamental frequency in sound; f0et: maximum error accepted in f0 detection algorithm                                                                                            
	harmDevSlope: allowed deviation of harmonic tracks, higher harmonics could have higher allowed deviation
	"""

	# size of fft used in synthesis
	Ns = 512

	# hop size (has to be 1/4 of Ns)
	H = 128

	# read input sound
	(fs, x) = UF.wavread(inputFile)

	# compute analysis window
	w = get_window(window, M)

	# detect harmonics of input sound
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

	# synthesize the harmonics
	y = SM.sineModelSynth(hfreq, hmag, hphase, Ns, H, fs)  

	# output sound file (monophonic with sampling rate of 44100)
	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_harmonicModel.wav'

	# write the sound resulting from harmonic analysis
	UF.wavwrite(y, fs, outputFile)
	return x,fs,hfreq,y
def analysis(inputFile='../../sounds/vignesh.wav', window='blackman', M=1201, N=2048, t=-90, 
	minSineDur=0.1, nH=100, minf0=130, maxf0=300, f0et=7, harmDevSlope=0.01):
	"""
	Analyze a sound with the harmonic model
	inputFile: input sound file (monophonic with sampling rate of 44100)
	window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)	
	M: analysis window size 
	N: fft size (power of two, bigger or equal than M)
	t: magnitude threshold of spectral peaks 
	minSineDur: minimum duration of sinusoidal tracks
	nH: maximum number of harmonics
	minf0: minimum fundamental frequency in sound
	maxf0: maximum fundamental frequency in sound
	f0et: maximum error accepted in f0 detection algorithm                                                                                            
	harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
	returns inputFile: input file name; fs: sampling rate of input file, tfreq, 
						tmag: sinusoidal frequencies and magnitudes
	"""

	# size of fft used in synthesis
	Ns = 512

	# hop size (has to be 1/4 of Ns)
	H = 128

	# read input sound
	fs, x = UF.wavread(inputFile)

	# compute analysis window
	w = get_window(window, M)

	# compute the harmonic model of the whole sound
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

	# synthesize the sines without original phases
	y = SM.sineModelSynth(hfreq, hmag, np.array([]), Ns, H, fs)

	# output sound file (monophonic with sampling rate of 44100)
	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_harmonicModel.wav'

	# write the sound resulting from the inverse stft
	UF.wavwrite(y, fs, outputFile)

	# create figure to show plots
	plt.figure(figsize=(12, 9))

	# frequency range to plot
	maxplotfreq = 5000.0

	# plot the input sound
	plt.subplot(3,1,1)
	plt.plot(np.arange(x.size)/float(fs), x)
	plt.axis([0, x.size/float(fs), min(x), max(x)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('input sound: x')
	
	if (hfreq.shape[1] > 0):
		plt.subplot(3,1,2)
		tracks = np.copy(hfreq)
		numFrames = tracks.shape[0]
		frmTime = H*np.arange(numFrames)/float(fs)
		tracks[tracks<=0] = np.nan
		plt.plot(frmTime, tracks)
		plt.axis([0, x.size/float(fs), 0, maxplotfreq])
		plt.title('frequencies of harmonic tracks')

	# plot the output sound
	plt.subplot(3,1,3)
	plt.plot(np.arange(y.size)/float(fs), y)
	plt.axis([0, y.size/float(fs), min(y), max(y)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('output sound: y')

	plt.tight_layout()
	plt.show(block=False)

	return inputFile, fs, hfreq, hmag
Пример #22
0
def estimateInharmonicity(inputFile = '../../sounds/piano.wav', t1=0.1, t2=0.5, window='hamming', 
                            M=2048, N=2048, H=128, f0et=5.0, t=-90, minf0=130, maxf0=180, nH = 10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """    
    ### Your code here
    
    # 0. Read the audio file
    fs, x = UF.wavread(inputFile)
    
    # 1. Use harmonic model to to compute the harmonic frequencies and magnitudes
    harmDevSlope = 0.01
    minSineDur = 0.0
    w = get_window(window, M)
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

    # 2. Extract the segment in which you need to compute the inharmonicity.
    total_time = float(len(x)) / fs
    
    bin1 = np.ceil((t1 / total_time) * len(hfreq))
    bin2 = np.floor((t2 / total_time) * len(hfreq))
    
    harm_seg = hfreq[bin1:bin2+1]

    # 3. Compute the mean inharmonicity for the segment
    Inharm = []

    R = len(harm_seg[0])

    ran = np.arange(1, R)
    
    #(1/R) * np.sum(harm_seg[:][1:len(harm_seg[0])] - harm_seg[:][0])

    for i in range(len(harm_seg)):
        tot = 0.0
        RA = 0
        for r in range(R):
            if harm_seg[i][r] > 0.0:
                RA += 1
                tot += np.abs(harm_seg[i][r] - ((r+1) * harm_seg[i][0])) / float(r+1)
        
        Inharm.append((1.0/R) * tot)

    Inmean = (1.0/(bin2-bin1+1.0)) * np.sum(Inharm)

    return Inmean
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time

sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../software/models/'))

import stft as STFT
import utilFunctions as UF
import harmonicModel as HM


(fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../sounds/cello-double.wav'))
w = np.blackman(3501)
N = 2048*2
t = -100
nH = 100
minf0 = 140
maxf0 = 150
f0et = 10
minSineDur = .2
harmDevSlope = 0.001
Ns = 512
H = Ns/4

hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
y = HM.harmonicModelSynth(hfreq, hmag, hphase, Ns, H, fs)
xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
Пример #24
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    harmDevSlope = 0.01
    minSineDur = 0.0
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                                  maxf0, f0et, harmDevSlope,
                                                  minSineDur)
    print(xhfreq.shape)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    segmentStart = int(np.ceil(t1 * fs / H))
    segmentEnd = int(np.ceil(t2 * fs / H)) + 1
    xhFreqSegment = xhfreq[segmentStart:segmentEnd]
    print(segmentStart)
    print(segmentEnd)
    print(xhFreqSegment.shape)

    # 3. Compute the mean inharmonicity of the segment
    numberOfSamples = xhFreqSegment.shape[0]
    inharmonicityArr = np.zeros(numberOfSamples)
    #f0 = xhFreqSegment[0][0]
    for sample in range(0, numberOfSamples):
        f0 = xhFreqSegment[sample][0]
        harmArr = np.array(range(1, nH + 1))
        frArr = harmArr * f0
        festArr = xhFreqSegment[sample]
        freqNotFound = 0
        for festIx in range(len(festArr)):
            if festArr[festIx] < eps:
                festArr[festIx] = 0
                frArr[festIx] = 0
                freqNotFound += 1
        inhArr = np.abs(festArr - frArr) / harmArr
        inharmonicity = np.sum(inhArr) / (len(inhArr) - freqNotFound)
        inharmonicityArr[sample] = inharmonicity
        print("sample " + str(sample))
        print("f0 " + str(f0))
        print(harmArr)
        print(frArr)
        print(festArr)
        print(inhArr)
        print(inharmonicity)
        #for fest in festArr:
        #    if fest < eps:
        #        return "fasza"
        print("")
    print(inharmonicityArr)
    result = np.sum(inharmonicityArr) / len(inharmonicityArr)
    return result
def analysis(inputFile='../../sounds/vignesh.wav',
             window='blackman',
             M=1201,
             N=2048,
             t=-90,
             minSineDur=0.1,
             nH=100,
             minf0=130,
             maxf0=300,
             f0et=7,
             harmDevSlope=0.01):
    """
	Analyze a sound with the harmonic model
	inputFile: input sound file (monophonic with sampling rate of 44100)
	window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)	
	M: analysis window size 
	N: fft size (power of two, bigger or equal than M)
	t: magnitude threshold of spectral peaks 
	minSineDur: minimum duration of sinusoidal tracks
	nH: maximum number of harmonics
	minf0: minimum fundamental frequency in sound
	maxf0: maximum fundamental frequency in sound
	f0et: maximum error accepted in f0 detection algorithm                                                                                            
	harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
	returns inputFile: input file name; fs: sampling rate of input file, tfreq, 
						tmag: sinusoidal frequencies and magnitudes
	"""

    # size of fft used in synthesis
    Ns = 512

    # hop size (has to be 1/4 of Ns)
    H = 128

    # read input sound
    fs, x = UF.wavread(inputFile)

    # compute analysis window
    w = get_window(window, M)

    # compute the harmonic model of the whole sound
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                               maxf0, f0et, harmDevSlope,
                                               minSineDur)

    # synthesize the sines without original phases
    y = SM.sineModelSynth(hfreq, hmag, np.array([]), Ns, H, fs)

    # output sound file (monophonic with sampling rate of 44100)
    outputFile = 'output_sounds/' + os.path.basename(
        inputFile)[:-4] + '_harmonicModel.wav'

    # write the sound resulting from the inverse stft
    UF.wavwrite(y, fs, outputFile)

    # create figure to show plots
    plt.figure(figsize=(12, 9))

    # frequency range to plot
    maxplotfreq = 5000.0

    # plot the input sound
    plt.subplot(3, 1, 1)
    plt.plot(np.arange(x.size) / float(fs), x)
    plt.axis([0, x.size / float(fs), min(x), max(x)])
    plt.ylabel('amplitude')
    plt.xlabel('time (sec)')
    plt.title('input sound: x')

    if (hfreq.shape[1] > 0):
        plt.subplot(3, 1, 2)
        tracks = np.copy(hfreq)
        numFrames = tracks.shape[0]
        frmTime = H * np.arange(numFrames) / float(fs)
        tracks[tracks <= 0] = np.nan
        plt.plot(frmTime, tracks)
        plt.axis([0, x.size / float(fs), 0, maxplotfreq])
        plt.title('frequencies of harmonic tracks')

    # plot the output sound
    plt.subplot(3, 1, 3)
    plt.plot(np.arange(y.size) / float(fs), y)
    plt.axis([0, y.size / float(fs), min(y), max(y)])
    plt.ylabel('amplitude')
    plt.xlabel('time (sec)')
    plt.title('output sound: y')

    plt.tight_layout()
    plt.show(block=False)

    return inputFile, fs, hfreq, hmag
def analysis(inputFile='../../sounds/vignesh.wav', window='blackman', M=1201, N=2048, t=-90, 
	minSineDur=0.1, nH=100, minf0=130, maxf0=300, f0et=7, harmDevSlope=0.01):
	# analyze a sound with the harmonic model
	# inputFile: input sound file (monophonic with sampling rate of 44100)
	# window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)	
	# M: analysis window size 
	# N: fft size (power of two, bigger or equal than M)
	# t: magnitude threshold of spectral peaks 
	# minSineDur: minimum duration of sinusoidal tracks
	# nH: maximum number of harmonics
	# minf0: minimum fundamental frequency in sound
	# maxf0: maximum fundamental frequency in sound
	# f0et: maximum error accepted in f0 detection algorithm                                                                                            
	# harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
	# returns inputFile: input file name; fs: sampling rate of input file,
	#         tfreq, tmag: sinusoidal frequencies and magnitudes

	# size of fft used in synthesis
	Ns = 512

	# hop size (has to be 1/4 of Ns)
	H = 128

	# read input sound
	(fs, x) = UF.wavread(inputFile)

	# compute analysis window
	w = get_window(window, M)

	# compute the magnitude and phase spectrogram of input sound
	mX, pX = STFT.stftAnal(x, fs, w, N, H)

	# compute the harmonic model of the whole sound
	hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

	# synthesize the sines without original phases
	y = SM.sineModelSynth(hfreq, hmag, np.array([]), Ns, H, fs)

	# output sound file (monophonic with sampling rate of 44100)
	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_harmonicModel.wav'

	# write the sound resulting from the inverse stft
	UF.wavwrite(y, fs, outputFile)

	# --------- plotting --------------------

	# create figure to show plots
	plt.figure(figsize=(12, 9))

	# frequency range to plot
	maxplotfreq = 5000.0

	# plot the input sound
	plt.subplot(3,1,1)
	plt.plot(np.arange(x.size)/float(fs), x)
	plt.axis([0, x.size/float(fs), min(x), max(x)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('input sound: x')
		
	# plot the magnitude spectrogram
	plt.subplot(3,1,2)
	maxplotbin = int(N*maxplotfreq/fs)
	numFrames = int(mX[:,0].size)
	frmTime = H*np.arange(numFrames)/float(fs)                       
	binFreq = np.arange(maxplotbin+1)*float(fs)/N                         
	plt.pcolormesh(frmTime, binFreq, np.transpose(mX[:,:maxplotbin+1]))
	plt.autoscale(tight=True)
		
	# plot the sinusoidal frequencies on top of the spectrogram
	tracks = hfreq*np.less(hfreq, maxplotfreq)
	tracks[tracks<=0] = np.nan
	plt.plot(frmTime, tracks, color='k')
	plt.title('magnitude spectrogram + harmonic tracks')
	plt.autoscale(tight=True)

	# plot the output sound
	plt.subplot(3,1,3)
	plt.plot(np.arange(y.size)/float(fs), y)
	plt.axis([0, y.size/float(fs), min(y), max(y)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('output sound: y')

	plt.tight_layout()
	plt.show(block=False)

	return inputFile, fs, hfreq, hmag
Пример #27
0
import harmonicModel as HM


inputFile = '../../sounds/vignesh.wav'
window = 'blackman'
M = 1201
N = 2048
t = -90.0
minSineDur = 0.1
nH = 50
minf0 = 130
maxf0 = 300
f0et = 5
harmDevSlope = 0.1
#harmDevSlope = 0.001  # restricts deviation in higher frequencies

Ns = 512
H = 128  # 1/4 of Ns

fs, x = UF.wavread(inputFile)
w = get_window(window, M)

hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

numFrames = int(hfreq[:,0].size)
frmTime = H * np.arange(numFrames) / float(fs)
hfreq[hfreq<=0] = np.nan
plt.plot(frmTime, hfreq)
plt.show()

Пример #28
0
def main(
    inputFile="../../sounds/sax-phrase.wav",
    window="blackman",
    M=601,
    N=1024,
    t=-100,
    minSineDur=0.1,
    nH=100,
    minf0=350,
    maxf0=700,
    f0et=5,
    harmDevSlope=0.01,
):

    # ------- analysis parameters -------------------

    # inputFile: input sound file (monophonic with sampling rate of 44100)
    # window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
    # M: analysis window size
    # N: fft size (power of two, bigger or equal than M)
    # t: magnitude threshold of spectral peaks
    # minSineDur: minimum duration of sinusoidal tracks
    # nH: maximum number of harmonics
    # minf0: minimum fundamental frequency in sound
    # maxf0: maximum fundamental frequency in sound
    # f0et: maximum error accepted in f0 detection algorithm
    # harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation

    # size of fft used in synthesis
    Ns = 512

    # hop size (has to be 1/4 of Ns)
    H = 128

    # --------- computation -----------------

    # read input sound
    (fs, x) = UF.wavread(inputFile)

    # compute analysis window
    w = get_window(window, M)

    # find harmonics
    hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)

    # subtract harmonics from original sound
    xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)

    # compute spectrogram of residual
    mXr, pXr = STFT.stftAnal(xr, fs, w, N, H)

    # synthesize harmonic component
    yh = SM.sineModelSynth(hfreq, hmag, hphase, Ns, H, fs)

    # sum harmonics and residual
    y = xr[: min(xr.size, yh.size)] + yh[: min(xr.size, yh.size)]

    # output sound file (monophonic with sampling rate of 44100)
    outputFileSines = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_hprModel_sines.wav"
    outputFileResidual = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_hprModel_residual.wav"
    outputFile = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_hprModel.wav"

    # write sounds files for harmonics, residual, and the sum
    UF.wavwrite(yh, fs, outputFileSines)
    UF.wavwrite(xr, fs, outputFileResidual)
    UF.wavwrite(y, fs, outputFile)

    # --------- plotting --------------------

    # create figure to plot
    plt.figure(figsize=(12, 9))

    # frequency range to plot
    maxplotfreq = 5000.0

    # plot the input sound
    plt.subplot(3, 1, 1)
    plt.plot(np.arange(x.size) / float(fs), x)
    plt.axis([0, x.size / float(fs), min(x), max(x)])
    plt.ylabel("amplitude")
    plt.xlabel("time (sec)")
    plt.title("input sound: x")

    # plot the magnitude spectrogram of residual
    plt.subplot(3, 1, 2)
    maxplotbin = int(N * maxplotfreq / fs)
    numFrames = int(mXr[:, 0].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    binFreq = np.arange(maxplotbin + 1) * float(fs) / N
    plt.pcolormesh(frmTime, binFreq, np.transpose(mXr[:, : maxplotbin + 1]))
    plt.autoscale(tight=True)

    # plot harmonic frequencies on residual spectrogram
    harms = hfreq * np.less(hfreq, maxplotfreq)
    harms[harms == 0] = np.nan
    numFrames = int(harms[:, 0].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    plt.plot(frmTime, harms, color="k", ms=3, alpha=1)
    plt.xlabel("time(s)")
    plt.ylabel("frequency(Hz)")
    plt.autoscale(tight=True)
    plt.title("harmonics + residual spectrogram")

    # plot the output sound
    plt.subplot(3, 1, 3)
    plt.plot(np.arange(y.size) / float(fs), y)
    plt.axis([0, y.size / float(fs), min(y), max(y)])
    plt.ylabel("amplitude")
    plt.xlabel("time (sec)")
    plt.title("output sound: y")

    plt.tight_layout()
    plt.show()
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    hfreq, hmag, hphase = HM.harmonicModelAnal(x,
                                               fs,
                                               w,
                                               N,
                                               H,
                                               t,
                                               nH,
                                               minf0,
                                               maxf0,
                                               f0et,
                                               harmDevSlope=0.01,
                                               minSineDur=0.0)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    startFrame = int(np.ceil(t1 * fs / H))
    endFrame = int(np.floor(t2 * fs / H))
    fSeg = hfreq[startFrame:endFrame]

    # 3. Compute the mean inharmonicity of the segment
    row, col = fSeg.shape

    I = np.zeros(row)
    for l in range(row):
        nonZeroFreqs = np.where(fSeg[l, :] > 0.0)[0]
        nonZeroFreqs = np.delete(nonZeroFreqs, 0)
        for r in nonZeroFreqs:
            I[l] += np.abs(fSeg[l, r] - (r + 1) * fSeg[l, 0]) / float(r + 1)

        I[l] = I[l] / nH

    Imean = 1.0 / (endFrame - startFrame) * np.sum(I)

    return Imean
Пример #30
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    (fs, x) = UF.wavread(inputFile)  #reading inputFile
    w = get_window(window, M)  #obtaining analysis window

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    harmDevSlope = 0.01
    minSineDur = 0.0
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0,
                                                  maxf0, f0et, harmDevSlope,
                                                  minSineDur)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    totalTime = x.size / float(fs)

    startBin = int(np.ceil((t1 / totalTime) * len(xhfreq)))
    lastBin = int(np.ceil((t2 / totalTime) * len(xhfreq)))

    segment = xhfreq[startBin:lastBin]

    # 3. Compute the mean inharmonicity of the segment

    def inharmonicity(frame):
        count = 0.0
        for i in range(frame.size):
            count += np.abs(frame[i] - ((i + 1) * frame[0])) / (i + 1)
        return count / frame.size

    totalInharmonicity = 0.0
    for frame in segment:
        totalInharmonicity += inharmonicity(frame)
    meanInharmonicity = totalInharmonicity / len(segment)

    return meanInharmonicity
Пример #31
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x,
                                                  fs,
                                                  w,
                                                  N,
                                                  H,
                                                  t,
                                                  nH,
                                                  minf0,
                                                  maxf0,
                                                  f0et,
                                                  harmDevSlope=0.01,
                                                  minSineDur=0.0)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    frameStart = int(np.ceil(
        t1 * fs / float(H)))  #get starting frame, round up and convert to int
    frameEnd = int(
        t2 * fs / float(H))  #get ending frame, round down by converting to int

    # 3. Compute the mean inharmonicity of the segment
    meanInharm = 0.0
    sum = 0.0  #store inharmonic of 1 frame
    for i in range(frameStart, frameEnd + 1):  #iterate through frames
        sum = 0.0  #reset sum for a new frame
        for j in range(1, nH):
            sum += abs(xhfreq[i, j] - (j + 1) * xhfreq[i, 0]) / (j + 1)
        sum /= nH  #divide sum by no. of harmonics
        meanInharm += sum
    meanInharm /= (frameEnd - frameStart + 1)
    return meanInharm
Пример #32
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=0.1,
                          t2=0.5,
                          window='hamming',
                          M=2048,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=130,
                          maxf0=180,
                          nH=10):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window

    fs, x = UF.wavread(inputFile)

    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    xhfreq, xhmag, xhphase = HM.harmonicModelAnal(x,
                                                  fs,
                                                  w,
                                                  N,
                                                  H,
                                                  t,
                                                  nH,
                                                  minf0,
                                                  maxf0,
                                                  f0et,
                                                  harmDevSlope=0.01,
                                                  minSineDur=0.0)

    # 2. Extract the time segment in which you need to compute the inharmonicity.

    interval_start = int(math.ceil(t1 * fs / float(H)))
    interval_end = int(math.ceil(t2 * fs / float(H)))

    # 3. Compute the mean inharmonicity of the segment

    # Refer to the pdf for the formulas used

    f0 = HM.f0Detection(x, fs, w, N, H, t, minf0, maxf0, f0et)

    f0_slice = f0[interval_start:interval_end]
    sliced = xhfreq[interval_start:interval_end]
    inharmon = np.zeros(sliced.size)

    for index, arr in enumerate(sliced):
        tmp_sum = 0

        for j in range(1, arr.size):
            val = j + 1
            tmp_sum += np.abs(arr[j] - val * f0_slice[index]) / float(val)

        inharmon[index] = tmp_sum * (1 / float(nH))

    mean_inharmon = sum(inharmon) / (interval_end - interval_start + 1)

    return mean_inharmon
Пример #33
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=2.3,
                          t2=2.55,
                          window='hamming',
                          M=2047,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=230,
                          maxf0=290,
                          nH=15):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    fs, x = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    xhfreq, xhmag, xhphas = HM.harmonicModelAnal(x,
                                                 fs,
                                                 w,
                                                 N,
                                                 H,
                                                 t,
                                                 nH,
                                                 minf0,
                                                 maxf0,
                                                 f0et,
                                                 harmDevSlope=0.01,
                                                 minSineDur=0.0)
    # 2. Extract the time segment in which you need to compute the inharmonicity.
    start = int(np.ceil(t1 * fs / float(H)))
    end = int(np.floor(t2 * fs / float(H)))
    frame_list = [start, end]
    print(start, end)

    # 3. Compute the mean inharmonicity of the segment
    l = []
    for val in range(start, end + 1):
        frame = xhfreq[val]
        #print(val)
        sum = 0
        count = 1
        for index, freq in enumerate(frame):
            #print(index)
            if index == 0:
                f0_freq = freq
                #print(f0_freq)
            else:
                if (freq != 0.0):
                    count += 1
                    sum = (sum + abs(freq -
                                     (index + 1) * f0_freq) / float(index + 1))
        l.append(sum / float(count))

    Imean = (np.sum(l)) / float(end - start + 1)

    return (Imean)
Пример #34
0
def estimateInharmonicity(inputFile='../../sounds/piano.wav',
                          t1=2.55,
                          t2=2.8,
                          window='hamming',
                          M=2047,
                          N=2048,
                          H=128,
                          f0et=5.0,
                          t=-90,
                          minf0=230,
                          maxf0=290,
                          nH=5):
    """
    Function to estimate the extent of inharmonicity present in a sound
    Input:
        inputFile (string): wav file including the path
        t1 (float): start time of the segment considered for computing inharmonicity
        t2 (float): end time of the segment considered for computing inharmonicity
        window (string): analysis window
        M (integer): window size used for computing f0 contour
        N (integer): FFT size used for computing f0 contour
        H (integer): Hop size used for computing f0 contour
        f0et (float): error threshold used for the f0 computation
        t (float): magnitude threshold in dB used in spectral peak picking
        minf0 (float): minimum fundamental frequency in Hz
        maxf0 (float): maximum fundamental frequency in Hz
        nH (integer): number of integers considered for computing inharmonicity
    Output:
        meanInharm (float or np.float): mean inharmonicity over all the frames between the time interval 
                                        t1 and t2. 
    """
    # 0. Read the audio file and obtain an analysis window
    (fs, x) = UF.wavread(inputFile)
    w = get_window(window, M)

    # 1. Use harmonic model to compute the harmonic frequencies and magnitudes
    hfreq, hmag, hphase = HM.harmonicModelAnal(x,
                                               fs,
                                               w,
                                               N,
                                               H,
                                               t,
                                               nH,
                                               minf0,
                                               maxf0,
                                               f0et,
                                               harmDevSlope=0.01,
                                               minSineDur=0.0)

    # 2. Extract the time segment in which you need to compute the inharmonicity.
    I = np.zeros(hfreq.size / nH)
    for i in range((hfreq.size / nH)):
        for j in range(1, nH + 1):
            I[i] = I[i] + abs(hfreq[i, j - 1] - j * hfreq[i, 0]) / j
        I[i] = I[i] / nH

    # 3. Compute the mean inharmonicity of the segments
    l2 = int(np.floor(t2 * fs / (x.size / (hfreq.size / nH))))
    l1 = int(np.ceil(t1 * fs / (x.size / (hfreq.size / nH))))
    Iseg = I[l1:l2 + 1]
    Imean = sum(Iseg) / (l2 - l1 + 1)

    return Imean