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-rw-r--r--test/sns.py263
1 files changed, 162 insertions, 101 deletions
diff --git a/test/sns.py b/test/sns.py
index 8740aab..641d99f 100644
--- a/test/sns.py
+++ b/test/sns.py
@@ -28,6 +28,7 @@ class Sns:
self.dt = dt
self.sr = sr
+ self.I = T.I[dt][sr]
(self.ind_lf, self.ind_hf, self.shape, self.gain) = \
(None, None, None, None)
@@ -52,7 +53,7 @@ class Sns:
def spectral_shaping(self, scf, inv, x):
- ## 3.3.7.4 Scale factors interpolation
+ ## Scale factors interpolation
scf_i = np.empty(4*len(scf))
scf_i[0 ] = scf[0]
@@ -64,20 +65,35 @@ class Sns:
scf_i[62 ] = scf[15 ] + 1/8 * (scf[15] - scf[14 ])
scf_i[63 ] = scf[15 ] + 3/8 * (scf[15] - scf[14 ])
- n2 = 64 - min(len(x), 64)
+ nb = len(self.I) - 1
- for i in range(n2):
- scf_i[i] = 0.5 * (scf_i[2*i] + scf_i[2*i+1])
- scf_i = np.append(scf_i[:n2], scf_i[2*n2:])
+ if nb < 32:
+ n4 = round(abs(1-32/nb)*nb)
+ n2 = nb - n4
+
+ for i in range(n4):
+ scf_i[i] = np.mean(scf_i[4*i:4*i+4])
+
+ for i in range(n4, n4+n2):
+ scf_i[i] = np.mean(scf_i[2*n4+2*i:2*n4+2*i+2])
+
+ scf_i = scf_i[:n4+n2]
+
+ elif nb < 64:
+ n2 = 64 - nb
+
+ for i in range(n2):
+ scf_i[i] = np.mean(scf_i[2*i:2*i+2])
+ scf_i = np.append(scf_i[:n2], scf_i[2*n2:])
g_sns = np.power(2, [ -scf_i, scf_i ][inv])
- ## 3.3.7.4 Spectral shaping
+ ## Spectral shaping
y = np.empty(len(x))
- I = T.I[self.dt][self.sr]
+ I = self.I
- for b in range(len(g_sns)):
+ for b in range(nb):
y[I[b]:I[b+1]] = x[I[b]:I[b+1]] * g_sns[b]
return y
@@ -89,40 +105,55 @@ class SnsAnalysis(Sns):
super().__init__(dt, sr)
- def compute_scale_factors(self, e, att):
+ def compute_scale_factors(self, e, att, nbytes):
dt = self.dt
+ sr = self.sr
+ hr = self.sr >= T.SRATE_48K_HR
- ## 3.3.7.2.1 Padding
+ ## Padding
- n2 = 64 - len(e)
+ if len(e) < 32:
+ n4 = round(abs(1-32/len(e))*len(e))
+ n2 = len(e) - n4
- e = np.append(np.empty(n2), e)
- for i in range(n2):
- e[2*i+0] = e[2*i+1] = e[n2+i]
+ e = np.append(np.zeros(3*n4+n2), e)
+ for i in range(n4):
+ e[4*i+0] = e[4*i+1] = \
+ e[4*i+2] = e[4*i+3] = e[3*n4+n2+i]
- ## 3.3.7.2.2 Smoothing
+ for i in range(2*n4, 2*n4+n2):
+ e[2*i+0] = e[2*i+1] = e[2*n4+n2+i]
+
+ elif len(e) < 64:
+ n2 = 64 - len(e)
+
+ e = np.append(np.empty(n2), e)
+ for i in range(n2):
+ e[2*i+0] = e[2*i+1] = e[n2+i]
+
+ ## Smoothing
e_s = np.zeros(len(e))
e_s[0 ] = 0.75 * e[0 ] + 0.25 * e[1 ]
e_s[1:63] = 0.25 * e[0:62] + 0.5 * e[1:63] + 0.25 * e[2:64]
e_s[ 63] = 0.25 * e[ 62] + 0.75 * e[ 63]
- ## 3.3.7.2.3 Pre-emphasis
+ ## Pre-emphasis
- g_tilt = [ 14, 18, 22, 26, 30 ][self.sr]
+ g_tilt = [ 14, 18, 22, 26, 30, 30, 34 ][self.sr]
e_p = e_s * (10 ** ((np.arange(64) * g_tilt) / 630))
- ## 3.3.7.2.4 Noise floor
+ ## Noise floor
noise_floor = max(np.average(e_p) * (10 ** (-40/10)), 2 ** -32)
e_p = np.fmax(e_p, noise_floor * np.ones(len(e)))
- ## 3.3.7.2.5 Logarithm
+ ## Logarithm
e_l = np.log2(10 ** -31 + e_p) / 2
- ## 3.3.7.2.6 Band energy grouping
+ ## Band energy grouping
w = [ 1/12, 2/12, 3/12, 3/12, 2/12, 1/12 ]
@@ -131,18 +162,22 @@ class SnsAnalysis(Sns):
e_4[1:15] = [ np.sum(w * e_l[4*i-1:4*i+5]) for i in range(1, 15) ]
e_4[ 15] = np.sum(w[:5] * e_l[59:64]) + w[5] * e_l[63]
- ## 3.3.7.2.7 Mean removal and scaling, attack handling
+ ## Mean removal and scaling, attack handling
- scf = 0.85 * (e_4 - np.average(e_4))
+ cf = [ 0.85, 0.6 ][hr]
+ if hr and nbytes * 8 > [ 1150, 2300, 0, 4400 ][self.dt]:
+ cf *= [ 0.25, 0.35 ][ self.dt == T.DT_10M ]
+
+ scf = cf * (e_4 - np.average(e_4))
scf_a = np.zeros(len(scf))
- scf_a[0 ] = np.average(scf[:3])
- scf_a[1 ] = np.average(scf[:4])
- scf_a[2:14] = [ np.average(scf[i:i+5]) for i in range(12) ]
- scf_a[ 14] = np.average(scf[12:])
- scf_a[ 15] = np.average(scf[13:])
+ scf_a[0 ] = np.mean(scf[:3])
+ scf_a[1 ] = np.mean(scf[:4])
+ scf_a[2:14] = [ np.mean(scf[i:i+5]) for i in range(12) ]
+ scf_a[ 14] = np.mean(scf[12:])
+ scf_a[ 15] = np.mean(scf[13:])
- scf_a = (0.5 if self.dt == T.DT_10M else 0.3) * \
+ scf_a = (0.5 if self.dt != T.DT_7M5 else 0.3) * \
(scf_a - np.average(scf_a))
return scf_a if att else scf
@@ -167,7 +202,7 @@ class SnsAnalysis(Sns):
def quantize(self, scf):
- ## 3.3.7.3.2 Stage 1
+ ## Stage 1
dmse_lf = [ np.sum((scf[:8] - T.SNS_LFCB[i]) ** 2) for i in range(32) ]
dmse_hf = [ np.sum((scf[8:] - T.SNS_HFCB[i]) ** 2) for i in range(32) ]
@@ -178,19 +213,19 @@ class SnsAnalysis(Sns):
st1 = np.append(T.SNS_LFCB[self.ind_lf], T.SNS_HFCB[self.ind_hf])
r1 = scf - st1
- ## 3.3.7.3.3 Stage 2
+ ## Stage 2
t2_rot = fftpack.dct(r1, norm = 'ortho')
x = np.abs(t2_rot)
- ## 3.3.7.3.3 Stage 2 Shape search, step 1
+ ## Stage 2 Shape search, step 1
K = 6
proj_fac = (K - 1) / sum(np.abs(t2_rot))
y3 = np.floor(x * proj_fac).astype(int)
- ## 3.3.7.3.3 Stage 2 Shape search, step 2
+ ## Stage 2 Shape search, step 2
corr_xy = np.sum(y3 * x)
energy_y = np.sum(y3 * y3)
@@ -204,7 +239,7 @@ class SnsAnalysis(Sns):
energy_y += 2*y3[n_best] + 1
y3[n_best] += 1
- ## 3.3.7.3.3 Stage 2 Shape search, step 3
+ ## Stage 2 Shape search, step 3
K = 8
@@ -219,16 +254,16 @@ class SnsAnalysis(Sns):
y2[n_best] += 1
- ## 3.3.7.3.3 Stage 2 Shape search, step 4
+ ## Stage 2 Shape search, step 4
y1 = np.append(y2[:10], [0] * 6)
- ## 3.3.7.3.3 Stage 2 Shape search, step 5
+ ## Stage 2 Shape search, step 5
corr_xy -= sum(y2[10:] * x[10:])
energy_y -= sum(y2[10:] * y2[10:])
- ## 3.3.7.3.3 Stage 2 Shape search, step 6
+ ## Stage 2 Shape search, step 6
K = 10
@@ -240,7 +275,7 @@ class SnsAnalysis(Sns):
energy_y += 2*y1[n_best] + 1
y1[n_best] += 1
- ## 3.3.7.3.3 Stage 2 Shape search, step 7
+ ## Stage 2 Shape search, step 7
y0 = np.append(y1[:10], [ 0 ] * 6)
@@ -249,18 +284,18 @@ class SnsAnalysis(Sns):
y0[n_best] += 1
- ## 3.3.7.3.3 Stage 2 Shape search, step 8
+ ## Stage 2 Shape search, step 8
y0 *= np.sign(t2_rot).astype(int)
y1 *= np.sign(t2_rot).astype(int)
y2 *= np.sign(t2_rot).astype(int)
y3 *= np.sign(t2_rot).astype(int)
- ## 3.3.7.3.3 Stage 2 Shape search, step 9
+ ## Stage 2 Shape search, step 9
xq = [ y / np.sqrt(sum(y ** 2)) for y in (y0, y1, y2, y3) ]
- ## 3.3.7.3.3 Shape and gain combination determination
+ ## Shape and gain combination determination
G = [ T.SNS_VQ_REG_ADJ_GAINS, T.SNS_VQ_REG_LF_ADJ_GAINS,
T.SNS_VQ_NEAR_ADJ_GAINS, T.SNS_VQ_FAR_ADJ_GAINS ]
@@ -273,7 +308,7 @@ class SnsAnalysis(Sns):
gain = G[self.shape][self.gain]
- ## 3.3.7.3.3 Enumeration of the selected PVQ pulse configurations
+ ## Enumeration of the selected PVQ pulse configurations
if self.shape == 0:
(self.idx_a, self.ls_a) = self.enum_mpvq(y0[:10])
@@ -288,15 +323,15 @@ class SnsAnalysis(Sns):
(self.idx_a, self.ls_a) = self.enum_mpvq(y3)
(self.idx_b, self.ls_b) = (None, None)
- ## 3.3.7.3.4 Synthesis of the Quantized scale factor
+ ## Synthesis of the Quantized scale factor
scf_q = st1 + gain * fftpack.idct(xq[self.shape], norm = 'ortho')
return scf_q
- def run(self, eb, att, x):
+ def run(self, eb, att, nbytes, x):
- scf = self.compute_scale_factors(eb, att)
+ scf = self.compute_scale_factors(eb, att, nbytes)
scf_q = self.quantize(scf)
y = self.spectral_shaping(scf_q, False, x)
@@ -372,7 +407,7 @@ class SnsSynthesis(Sns):
def unquantize(self):
- ## 3.7.4.2.1-2 SNS VQ Decoding
+ ## SNS VQ Decoding
y = np.empty(16, dtype=np.intc)
@@ -387,11 +422,11 @@ class SnsSynthesis(Sns):
elif self.shape == 3:
y = self.deenum_mpvq(self.idx_a, self.ls_a, 6, 16)
- ## 3.7.4.2.3 Unit energy normalization
+ ## Unit energy normalization
y = y / np.sqrt(sum(y ** 2))
- ## 3.7.4.2.4 Reconstruction of the quantized scale factors
+ ## Reconstruction of the quantized scale factors
G = [ T.SNS_VQ_REG_ADJ_GAINS, T.SNS_VQ_REG_LF_ADJ_GAINS,
T.SNS_VQ_NEAR_ADJ_GAINS, T.SNS_VQ_FAR_ADJ_GAINS ]
@@ -464,20 +499,36 @@ def check_analysis(rng, dt, sr):
analysis = SnsAnalysis(dt, sr)
for i in range(10):
- x = rng.random(T.NE[dt][sr]) * 1e4
- e = rng.random(min(len(x), 64)) * 1e10
+ ne = T.I[dt][sr][-1]
+ x = rng.random(ne) * 1e4
+ e = rng.random(len(T.I[dt][sr]) - 1) * 1e10
+
+ if sr >= T.SRATE_48K_HR:
+ for nbits in (1144, 1152, 2296, 2304, 4400, 4408):
+ y = analysis.run(e, False, nbits // 8, x)
+ data = analysis.get_data()
- for att in (0, 1):
- y = analysis.run(e, att, x)
- data = analysis.get_data()
+ (y_c, data_c) = lc3.sns_analyze(
+ dt, sr, nbits // 8, e, False, x)
- (y_c, data_c) = lc3.sns_analyze(dt, sr, e, att, x)
+ for k in data.keys():
+ ok = ok and data_c[k] == data[k]
- for k in data.keys():
- ok = ok and data_c[k] == data[k]
+ ok = ok and lc3.sns_get_nbits() == analysis.get_nbits()
+ ok = ok and np.amax(np.abs(y - y_c)) < 1e-1
- ok = ok and lc3.sns_get_nbits() == analysis.get_nbits()
- ok = ok and np.amax(np.abs(y - y_c)) < 1e-1
+ else:
+ for att in (0, 1):
+ y = analysis.run(e, att, 0, x)
+ data = analysis.get_data()
+
+ (y_c, data_c) = lc3.sns_analyze(dt, sr, 0, e, att, x)
+
+ for k in data.keys():
+ ok = ok and data_c[k] == data[k]
+
+ ok = ok and lc3.sns_get_nbits() == analysis.get_nbits()
+ ok = ok and np.amax(np.abs(y - y_c)) < 1e-1
return ok
@@ -502,76 +553,81 @@ def check_synthesis(rng, dt, sr):
synthesis.idx_b = rng.integers(0, sz_shape_b, endpoint=True)
synthesis.ls_b = bool(rng.integers(0, 1, endpoint=True))
- x = rng.random(T.NE[dt][sr]) * 1e4
+ ne = T.I[dt][sr][-1]
+ x = rng.random(ne) * 1e4
y = synthesis.run(x)
y_c = lc3.sns_synthesize(dt, sr, synthesis.get_data(), x)
- ok = ok and np.amax(np.abs(y - y_c)) < 2e0
+ ok = ok and np.amax(np.abs(1 - y/y_c)) < 1e-5
return ok
def check_analysis_appendix_c(dt):
+ i0 = dt - T.DT_7M5
sr = T.SRATE_16K
+
ok = True
- for i in range(len(C.E_B[dt])):
+ for i in range(len(C.E_B[i0])):
- scf = lc3.sns_compute_scale_factors(dt, sr, C.E_B[dt][i], False)
- ok = ok and np.amax(np.abs(scf - C.SCF[dt][i])) < 1e-4
+ scf = lc3.sns_compute_scale_factors(dt, sr, 0, C.E_B[i0][i], False)
+ ok = ok and np.amax(np.abs(scf - C.SCF[i0][i])) < 1e-4
(lf, hf) = lc3.sns_resolve_codebooks(scf)
- ok = ok and lf == C.IND_LF[dt][i] and hf == C.IND_HF[dt][i]
+ ok = ok and lf == C.IND_LF[i0][i] and hf == C.IND_HF[i0][i]
(y, yn, shape, gain) = lc3.sns_quantize(scf, lf, hf)
- ok = ok and np.any(y[0][:16] - C.SNS_Y0[dt][i] == 0)
- ok = ok and np.any(y[1][:10] - C.SNS_Y1[dt][i] == 0)
- ok = ok and np.any(y[2][:16] - C.SNS_Y2[dt][i] == 0)
- ok = ok and np.any(y[3][:16] - C.SNS_Y3[dt][i] == 0)
- ok = ok and shape == 2*C.SUBMODE_MSB[dt][i] + C.SUBMODE_LSB[dt][i]
- ok = ok and gain == C.G_IND[dt][i]
+ ok = ok and np.any(y[0][:16] - C.SNS_Y0[i0][i] == 0)
+ ok = ok and np.any(y[1][:10] - C.SNS_Y1[i0][i] == 0)
+ ok = ok and np.any(y[2][:16] - C.SNS_Y2[i0][i] == 0)
+ ok = ok and np.any(y[3][:16] - C.SNS_Y3[i0][i] == 0)
+ ok = ok and shape == 2*C.SUBMODE_MSB[i0][i] + C.SUBMODE_LSB[i0][i]
+ ok = ok and gain == C.G_IND[i0][i]
scf_q = lc3.sns_unquantize(lf, hf, yn[shape], shape, gain)
- ok = ok and np.amax(np.abs(scf_q - C.SCF_Q[dt][i])) < 1e-5
-
- x = lc3.sns_spectral_shaping(dt, sr, C.SCF_Q[dt][i], False, C.X[dt][i])
- ok = ok and np.amax(np.abs(1 - x/C.X_S[dt][i])) < 1e-5
-
- (x, data) = lc3.sns_analyze(dt, sr, C.E_B[dt][i], False, C.X[dt][i])
- ok = ok and data['lfcb'] == C.IND_LF[dt][i]
- ok = ok and data['hfcb'] == C.IND_HF[dt][i]
- ok = ok and data['shape'] == \
- 2*C.SUBMODE_MSB[dt][i] + C.SUBMODE_LSB[dt][i]
- ok = ok and data['gain'] == C.G_IND[dt][i]
- ok = ok and data['idx_a'] == C.IDX_A[dt][i]
- ok = ok and data['ls_a'] == C.LS_IND_A[dt][i]
- ok = ok and (C.IDX_B[dt][i] is None or
- data['idx_b'] == C.IDX_B[dt][i])
- ok = ok and (C.LS_IND_B[dt][i] is None or
- data['ls_b'] == C.LS_IND_B[dt][i])
- ok = ok and np.amax(np.abs(1 - x/C.X_S[dt][i])) < 1e-5
+ ok = ok and np.amax(np.abs(scf_q - C.SCF_Q[i0][i])) < 1e-5
+
+ x = lc3.sns_spectral_shaping(dt, sr, C.SCF_Q[i0][i], False, C.X[i0][i])
+ ok = ok and np.amax(np.abs(1 - x/C.X_S[i0][i])) < 1e-5
+
+ (x, data) = lc3.sns_analyze(dt, sr, 0, C.E_B[i0][i], False, C.X[i0][i])
+ ok = ok and data['lfcb'] == C.IND_LF[i0][i]
+ ok = ok and data['hfcb'] == C.IND_HF[i0][i]
+ ok = ok and data['shape'] == 2*C.SUBMODE_MSB[i0][i] + \
+ C.SUBMODE_LSB[i0][i]
+ ok = ok and data['gain'] == C.G_IND[i0][i]
+ ok = ok and data['idx_a'] == C.IDX_A[i0][i]
+ ok = ok and data['ls_a'] == C.LS_IND_A[i0][i]
+ ok = ok and (C.IDX_B[i0][i] is None or
+ data['idx_b'] == C.IDX_B[i0][i])
+ ok = ok and (C.LS_IND_B[i0][i] is None or
+ data['ls_b'] == C.LS_IND_B[i0][i])
+ ok = ok and np.amax(np.abs(1 - x/C.X_S[i0][i])) < 1e-5
return ok
def check_synthesis_appendix_c(dt):
+ i0 = dt - T.DT_7M5
sr = T.SRATE_16K
+
ok = True
- for i in range(len(C.X_HAT_TNS[dt])):
+ for i in range(len(C.X_HAT_TNS[i0])):
data = {
- 'lfcb' : C.IND_LF[dt][i], 'hfcb' : C.IND_HF[dt][i],
- 'shape' : 2*C.SUBMODE_MSB[dt][i] + C.SUBMODE_LSB[dt][i],
- 'gain' : C.G_IND[dt][i],
- 'idx_a' : C.IDX_A[dt][i],
- 'ls_a' : C.LS_IND_A[dt][i],
- 'idx_b' : C.IDX_B[dt][i] if C.IDX_B[dt][i] is not None else 0,
- 'ls_b' : C.LS_IND_B[dt][i] if C.LS_IND_B[dt][i] is not None else 0,
+ 'lfcb' : C.IND_LF[i0][i], 'hfcb' : C.IND_HF[i0][i],
+ 'shape' : 2*C.SUBMODE_MSB[i0][i] + C.SUBMODE_LSB[i0][i],
+ 'gain' : C.G_IND[i0][i],
+ 'idx_a' : C.IDX_A[i0][i],
+ 'ls_a' : C.LS_IND_A[i0][i],
+ 'idx_b' : C.IDX_B[i0][i] if C.IDX_B[i0][i] is not None else 0,
+ 'ls_b' : C.LS_IND_B[i0][i] if C.LS_IND_B[i0][i] is not None else 0,
}
- x = lc3.sns_synthesize(dt, sr, data, C.X_HAT_TNS[dt][i])
- ok = ok and np.amax(np.abs(x - C.X_HAT_SNS[dt][i])) < 1e0
+ x = lc3.sns_synthesize(dt, sr, data, C.X_HAT_TNS[i0][i])
+ ok = ok and np.amax(np.abs(x - C.X_HAT_SNS[i0][i])) < 1e0
return ok
@@ -581,13 +637,18 @@ def check():
ok = True
for dt in range(T.NUM_DT):
- for sr in range(T.NUM_SRATE):
+ for sr in range(T.SRATE_8K, T.SRATE_48K + 1):
ok = ok and check_analysis(rng, dt, sr)
ok = ok and check_synthesis(rng, dt, sr)
- for dt in range(T.NUM_DT):
- ok = ok and check_analysis_appendix_c(dt)
- ok = ok and check_synthesis_appendix_c(dt)
+ for dt in ( T.DT_2M5, T.DT_5M, T.DT_10M ):
+ for sr in ( T.SRATE_48K_HR, T.SRATE_96K_HR ):
+ ok = ok and check_analysis(rng, dt, sr)
+ ok = ok and check_synthesis(rng, dt, sr)
+
+ for dt in ( T.DT_7M5, T.DT_10M ):
+ check_analysis_appendix_c(dt)
+ check_synthesis_appendix_c(dt)
return ok
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-28 10:21:52 +0000