Datasets:
Sonotheia
/
sonotheia-baselines

Dataset card Data Studio Files
xet
 Community
2
name
stringclasses
6 values
experiment_id
int64
18
44
lock_date
timestamp[s]date
2026-02-18 00:00:00
2026-02-26 00:00:00
overall_eer
float64
3.13
4.7
mean_attack_eer
float64
1.91
3.09
worst_eer
float64
7.53
18.3
worst_attack
stringclasses
3 values
per_attack_eer
dict
architecture
stringclasses
6 values
codecs
listlengths
3
3
feature_types
listlengths
5
8
baseline_role
stringclasses
1 value
canonical
bool
1 class
9sys_+flux+f0
18
2026-02-18T00:00:00
4.44
3.09
14.63
A18
{ "A07": 1.11, "A08": 0.34, "A09": 0.02, "A10": 2.3, "A11": 0.42, "A12": 1.27, "A13": 0.14, "A14": 5.55, "A15": 2.36, "A16": 1.17, "A17": 11.48, "A18": 14.63, "A19": 0.38 }
9 classifiers (3 codecs x 3 core features: LFCC/CQCC/MGDCC). Each classifier's input = core_feature(360-dim) + spectral_flux(12-dim) + f0_trajectory(24-dim) = 396-dim. Per-system StandardScaler, LogisticRegression(C=0.1, solver=lbfgs, max_iter=2000, seed=42). Min-max score normalization per system, avg_score fusion across 9 systems.
[ "wideband", "g711_ulaw", "amr_nb" ]
[ "lfcc", "cqcc", "mgdcc", "spectral_flux", "f0_trajectory" ]
canonical
true
mega_all5
18
2026-02-18T00:00:00
4.7
3.04
18.31
A14
{ "A07": 0.78, "A08": 0.26, "A09": 0, "A10": 1.85, "A11": 0.32, "A12": 0.95, "A13": 0.09, "A14": 18.31, "A15": 1.99, "A16": 0.8, "A17": 5.73, "A18": 8.06, "A19": 0.42 }
3 mega-classifiers (1 per codec). Each classifier's input = LFCC(360) + CQCC(360) + MGDCC(360) + spectral_flux(12) + f0_trajectory(24) = 1116-dim. Per-codec StandardScaler, LogisticRegression(C=0.1, solver=lbfgs, max_iter=2000, seed=42). Min-max score normalization per codec, avg_score fusion across 3 codecs.
[ "wideband", "g711_ulaw", "amr_nb" ]
[ "lfcc", "cqcc", "mgdcc", "spectral_flux", "f0_trajectory" ]
canonical
true
delta_pareto_9sys
20
2026-02-19T00:00:00
4.15
2.76
12.33
A17
{ "A07": 0.68, "A08": 0.22, "A09": 0.01, "A10": 2.9, "A11": 0.29, "A12": 0.82, "A13": 0.08, "A14": 3.7, "A15": 2.3, "A16": 0.78, "A17": 12.33, "A18": 8.4, "A19": 0.28 }
9 classifiers (3 codecs x 3 core features: LFCC/CQCC/MGDCC). Each classifier's input = delta(static+Δ+ΔΔ, 1080-dim) + spectral_flux(12-dim) + f0_trajectory(24-dim) = 1116-dim. Per-system StandardScaler, LogisticRegression(C=0.1, solver=lbfgs, max_iter=2000, seed=42). Min-max score normalization per system, Pareto-weighted avg_score fusion (wideband=1.5, g711_ulaw=1.5, amr_nb=1.0).
[ "wideband", "g711_ulaw", "amr_nb" ]
[ "lfcc", "cqcc", "mgdcc", "spectral_flux", "f0_trajectory" ]
canonical
true
exp37b_blend_1500d
37
2026-02-22T00:00:00
3.13
1.91
9.88
A17
{ "A07": 0.56, "A08": 0.7, "A09": 0, "A10": 1.45, "A11": 0, "A12": 0.5, "A13": 0, "A14": 1.78, "A15": 1.82, "A16": 0.7, "A17": 9.88, "A18": 6.05, "A19": 1.45 }
27 classifiers (3 codecs x 3 core features x 3 classifier types). Each classifier's input = delta(static+Δ+ΔΔ, 1080-dim) + spectral_flux(12-dim) + f0_trajectory(24-dim) + IFCC_delta(360-dim) + HF/LF_comod(24-dim) = 1500-dim. Three classifier types per (codec, core_feature): (1) Standard LR(C=0.1, solver=lbfgs, max_iter=2000, seed=42); (2) OHEM-LR: two-pass LR with loss-based sample weights from pass-1 (cap=5.0); (3) Shallow LGBM(n_estimators=200, max_depth=4, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8, seed=42). Per-system StandardScaler, min-max score normalization. Pareto-weighted avg_score per classifier type (wideband=1.5, g711_ulaw=1.5, amr_nb=1.0). 3-component blend: score = 0.66 × OHEM + 0.04 × LGBM + 0.30 × LR.
[ "wideband", "g711_ulaw", "amr_nb" ]
[ "lfcc", "cqcc", "mgdcc", "spectral_flux", "f0_trajectory", "ifcc", "hf_lf_comod" ]
canonical
true
exp40_cqcc_blend
40
2026-02-24T00:00:00
3.35
2.4
7.53
A17
{ "A07": 1.46, "A08": 1.47, "A09": 0.01, "A10": 2.45, "A11": 0.24, "A12": 0.1, "A13": 0.01, "A14": 2.41, "A15": 3.06, "A16": 1.37, "A17": 7.53, "A18": 6.62, "A19": 4.48 }
27 classifiers (3 codecs x 3 core features x 3 classifier types). Each classifier's input = delta(static+Δ+ΔΔ, 1080-dim) + spectral_flux(12-dim) + f0_trajectory(24-dim) + IFCC_delta(360-dim) + HF/LF_comod(24-dim) = 1500-dim. Three classifier types per (codec, core_feature): (1) Standard LR(C=0.1, solver=lbfgs, max_iter=2000, seed=42); (2) OHEM-LR: two-pass LR with loss-based sample weights from pass-1 (cap=5.0); (3) Shallow LGBM(n_estimators=200, max_depth=4, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8, seed=42). Per-system StandardScaler, min-max score normalization. CQCC-weighted Pareto fusion (CQCC=1.5, LFCC=0.75, MGDCC=0.25) with codec weights (wideband=1.5, g711_ulaw=1.5, amr_nb=1.0). 3-component blend: score = 0.60 × OHEM + 0.16 × LGBM + 0.24 × LR.
[ "wideband", "g711_ulaw", "amr_nb" ]
[ "lfcc", "cqcc", "mgdcc", "spectral_flux", "f0_trajectory", "ifcc", "hf_lf_comod" ]
canonical
true
exp44_scc_cqcc_blend
44
2026-02-26T00:00:00
3.48
2.54
7.83
A17
{ "A07": 2.15, "A08": 1.88, "A09": 0.02, "A10": 2.62, "A11": 0.13, "A12": 0.14, "A13": 0.14, "A14": 2.65, "A15": 2.9, "A16": 1.85, "A17": 7.83, "A18": 5.25, "A19": 5.5 }
27 classifiers (3 codecs x 3 core features x 3 classifier types). Each classifier's input = delta(static+Δ+ΔΔ, 1080-dim) + spectral_flux(12-dim) + f0_trajectory(24-dim) + IFCC_delta(360-dim) + HF/LF_comod(24-dim) + SCC_delta(360-dim) = 1860-dim. SCC = wavelet scattering transform (J=2, Q=10, order=2) → log → DCT (20 coeffs). Three classifier types per (codec, core_feature): (1) Standard LR(C=0.1, solver=lbfgs, max_iter=2000, seed=42); (2) OHEM-LR: two-pass LR with loss-based sample weights from pass-1 (cap=5.0); (3) Shallow LGBM(n_estimators=200, max_depth=4, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8, seed=42). Per-system StandardScaler, Platt-calibrated scores (LogisticRegression on 1D). CQCC-weighted Pareto fusion (CQCC=2.5, LFCC=0.5, MGDCC=0.25, strong-B) with codec weights (wideband=1.5, g711_ulaw=1.5, amr_nb=1.0). 3-component blend: score = 0.47 × OHEM + 0.15 × LGBM + 0.38 × LR.
[ "wideband", "g711_ulaw", "amr_nb" ]
[ "lfcc", "cqcc", "mgdcc", "spectral_flux", "f0_trajectory", "ifcc", "hf_lf_comod", "scc_delta" ]
canonical
true