def ltsd(self, s):
x = sigutil.framesig(s, self.Nw, self.Nsh, self.win)
X = np.apply_along_axis(sp.spect_power, 1, x, fs, Nw)
L = len(X)
d = np.zeros(len(l))
flag = np.zeros(len(l))
for l in range(self.M, L-self.M):
ltse = np.amax(X[:,l-self.M:l+self.M],1)
d[l] = 10*math.log10(ltse**2 * (1.0/self.N**2)/self.NFFT)
def __init__(NFFT, fs, Nw, Nsh, n, M, K=3):
self.NFFT = NFFT
self.fs = fs
self.Nw = Nw
self.Nsh = Nsh
self.win = signal.hamming(Nw)
self.K = K
self.alpha = 0.95
n = sigutil.framesig(n, Nw, Nsh, self.win)
N = np.apply_along_axis(sp.spect_power, 1, n, fs, NFFT)
self.N = np.mean(N[:K*2], 1) # Initial mean noise spectrum.
energy = np.sum(n[:K*2]**2, 0)
self.E0 = min(energy)
self.E1 = max(energy)
self.M = M
