def feature_gamma(audioname):
def princarg(p):
return ((p + np.pi) % -2 * np.pi) + np.pi
audiopath = get_audiopath(audioname, INST)
audiopath = get_audiopath(audioname, INST)
y, _ = librosa.load(path=audiopath, **AUDIO)
X_c = librosa.core.stft(y=y, **STFT).T
X_m = np.abs(X_c)
X_p = np.angle(X_c)
X_up = np.unwrap(X_p)
X_gamma = np.abs(X_m)
for t in range(2, X_gamma.shape[1]):
phi_target = princarg(2 * X_up[:, t - 1] - X_up[:, t - 2])
r_target = np.abs(X_m[:, t - 1])
phi_current = np.angle(X_c[:, t])
r_current = np.abs(X_c[:, t])
X_gamma[:, t] = np.sqrt(
r_target ** 2
+ r_current ** 2
- 2 * r_target * r_current * np.cos(phi_target - phi_current)
)
X = X_gamma
Y = get_targets(name).T
return X, Y
def feature_acd(audioname):
audiopath = get_audiopath(audioname, INST)
y, _ = librosa.load(path=audiopath, **AUDIO)
X_c = librosa.core.stft(y=y, **STFT).T
X_cd = deviation(X_c)
X_acd = np.abs(X_cd)
X = X_acd
Y = get_targets(name).T
return X, Y
def feature_m_md(audioname):
# get audiopath from audioname
audiopath = get_audiopath(audioname, INST)
y, _ = librosa.load(path=audiopath, **AUDIO)
X_c = librosa.core.stft(y=y, **STFT).T
X_m = np.abs(X_c)
X_md = deviation(X_m)
X = np.hstack((X_m, X_md))
Y = get_targets(name).T
return X, Y
def feature_m(audioname):
# get audiopath from audioname
audiopath = get_audiopath(audioname, INST)
y, _ = librosa.load(path=audiopath, **AUDIO)
X_c = librosa.core.stft(y=y, **STFT).T
# abs
X_m = np.abs(X_c)
X = X_m
Y = get_targets(name).T
return X, Y