def hpr_model_anal(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 = harmonicModel.harmonic_model_anal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
Ns = 512
xr = utilFunctions.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) # subtract sinusoids from original sound
return hfreq, hmag, hphase, xr
def hps_model_anal(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 = harmonicModel.harmonic_model_anal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur)
# subtract sinusoids from original sound
xr = utilFunctions.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs)
# perform stochastic analysis of residual
stocEnv = stochasticModel.stochastic_model_anal(xr, H, H * 2, stocf)
return hfreq, hmag, hphase, stocEnv
