| --- |
| license: other |
| license_name: multiple |
| task_categories: |
| - audio-classification |
| language: |
| - en |
| tags: |
| - audio |
| - codec |
| - speech |
| - channel-degradation |
| - deepfake-detection |
| - acoustic-features |
| - bicoherence |
| - CELP |
| - telecommunications |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # SSA Codec Degradation Study — Acoustic Feature Exports |
|
|
| **Moonscape Software | 2026** |
| *A companion to the Synthetic Speech Atlas (SSA)* |
|
|
| --- |
|
|
| ## Overview |
|
|
| This dataset quantifies the effect of 35 codec conditions on 80+ acoustic |
| features extracted from 7,500 biological speech clips. It answers the question: |
| **"Which acoustic features survive telecommunications codec compression, and which |
| are destroyed?"** |
|
|
| The corpus is the empirical foundation for channel-aware gate calibration in |
| deepfake audio detection. Every threshold in the SSA Gate 0 channel triage |
| architecture is derived from measurements in this dataset. |
|
|
| **262,463 rows | 35 codec conditions | 7,500 source clips | 4 speech corpora** |
|
|
| --- |
|
|
| ## Key Finding |
|
|
| **Phase-smear decoupling (bico_f0_f1) survives CELP codec compression at 96.3%.** |
|
|
| This is counterintuitive — CELP codecs (AMR-NB, GSM, G.711) completely destroy |
| and parametrically reconstruct the waveform. Yet the first formant bicoherence |
| is almost entirely preserved. |
|
|
| The physical reason: bico_f0_f1 measures **nonlinear quadratic phase coupling** |
| established at glottal closure. CELP codec operations are entirely **linear**. |
| Linear operations cannot create or destroy nonlinear phase relationships. |
|
|
| Contrast with modgd_var (spectral phase complexity — a linear property): |
| |
| | Codec Family | bico_f0_f1 | modgd_var | bico retained | modgd retained | |
| |-------------|-----------|-----------|--------------|----------------| |
| | Source (biological) | 0.465 | 5.332 | 100% | 100% | |
| | EVS SWB 48kbps | 0.456 | 5.362 | 98.1% | 100.6% | |
| | Opus 32kbps | 0.465 | 5.361 | 99.9% | 100.5% | |
| | AMR-NB 4.75kbps | 0.446 | 2.697 | **95.8%** | **50.6%** | |
| | GSM 13kbps | 0.446 | 2.804 | 95.8% | 52.6% | |
| | G.711 A-Law | 0.447 | 2.686 | 96.1% | 50.4% | |
| | Codec2 700bps | 0.475 | 2.770 | 102.1% | 51.9% | |
|
|
| **Implication for deployment:** bico_f0_f1-based deepfake detection works on |
| telephony audio, VoIP intercepts, and any channel condition. The detection |
| boundary is architectural — synthetic speech never had a biological glottis — |
| not environmental. |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| **Format:** Long — one row per clip per codec condition. |
| Use `codec_condition` column to filter to a specific codec. |
|
|
| ```python |
| import pandas as pd |
| df = pd.read_parquet('ssa_codec_degradation_study.parquet') |
| |
| # Compare CELP vs modern codecs on primary detection signal |
| df.groupby('is_celp')['bico_f0_f1'].mean() |
| |
| # Full degradation curve for modgd_var |
| df.groupby('codec_condition')['modgd_var'].mean().sort_values() |
| |
| # All AMR-NB 4.75kbps clips |
| amr = df[df['codec_condition'] == 'amr_nb_475'] |
| ``` |
|
|
| --- |
|
|
| ## Source Corpora |
|
|
| | Pool | Corpus | N clips | Licence | Recording conditions | |
| |------|--------|---------|---------|---------------------| |
| | VCTK_mic1 | VCTK 0.92 (MKH800) | 1,500 | CC-BY-4.0 | Anechoic studio, high-bandwidth | |
| | VCTK_mic2 | VCTK 0.92 (AKG C535) | 1,500 | CC-BY-4.0 | Anechoic studio, standard mic | |
| | AMI | AMI Meeting Corpus | 3,000 | CC-BY-4.0 | Spontaneous conversational | |
| | CREMA-D | CREMA-D | 1,500 | ODC-BY | Emotional speech, controlled | |
|
|
| **RAVDESS excluded:** CC-BY-NC-SA-4.0 licence would propagate NC to entire |
| dataset. RAVDESS-derived results available in the NC variant of this release. |
|
|
| All clips stratified by gender, source corpus, and emotional valence to ensure |
| biological diversity across the codec degradation curves. |
|
|
| --- |
|
|
| ## Codec Conditions |
|
|
| ### Single Codec (27 conditions) |
|
|
| | Condition | Family | Nom. Ceiling | Standard | |
| |-----------|--------|-------------|----------| |
| | source | Biological | — | Unencoded reference | |
| | evs_swb_48k | EVS-SWB | 16kHz | 3GPP TS 26.445 | |
| | evs_swb_nodtx | EVS-SWB | 16kHz | EVS without DTX | |
| | evs_24400 | EVS | 16kHz | 3GPP R12 | |
| | evs_24400_nodtx | EVS | 16kHz | EVS 24.4k without DTX | |
| | evs_9600 | EVS | 8kHz | 3GPP R12 | |
| | evs_9600_nodtx | EVS | 8kHz | EVS 9.6k without DTX | |
| | opus_32k | Opus-CELT | 16kHz | IETF RFC6716 | |
| | opus_16k | Opus-SILK | 16kHz | IETF RFC6716 | |
| | opus_6k | Opus-SILK | 8kHz | IETF RFC6716 | |
| | lc3 | LC3 | 16kHz | Bluetooth LE Audio | |
| | aac_64k | Perceptual | 16kHz | MPEG-4 | |
| | aac_32k | Perceptual | 16kHz | MPEG-4 | |
| | mp3_128k | Perceptual | 16kHz | MPEG-1 Layer III | |
| | mp3_32k | Perceptual | 16kHz | MPEG-1 Layer III | |
| | g722 | SB-ADPCM | 7kHz | ITU-T G.722 | |
| | amr_wb | CELP-WB | 7kHz | 3GPP AMR-WB | |
| | g726_32k | ADPCM | 4kHz | ITU-T G.726 | |
| | g726_24k | ADPCM | 4kHz | ITU-T G.726 | |
| | g726_16k | ADPCM | 4kHz | ITU-T G.726 | |
| | amr_nb_122 | CELP | 4kHz | 3GPP AMR-NB 12.2kbps | |
| | amr_nb_475 | CELP | 4kHz | 3GPP AMR-NB 4.75kbps | |
| | gsm | CELP | 4kHz | ETSI GSM 13kbps | |
| | ilbc | CELP | 4kHz | IETF RFC3951 | |
| | speex_8k | CELP | 4kHz | Xiph Speex 8kbps | |
| | g711_ulaw | PCM-Comp | 4kHz | ITU-T G.711 μ-Law | |
| | g711_alaw | PCM-Comp | 4kHz | ITU-T G.711 A-Law | |
| | codec2_700 | CELP | 4kHz | FreeDV Codec2 700bps | |
| |
| ### Tandem Chains (7 conditions) |
| Multi-hop codec degradation simulating real-world transmission paths: |
| |
| | Condition | Chain | Final ceiling | |
| |-----------|-------|--------------| |
| | tandem_opus_32k_to_evs_24400 | Opus 32k → EVS 24.4k | 16kHz | |
| | tandem_evs_24400_to_amr_wb | EVS 24.4k → AMR-WB | 7kHz | |
| | tandem_opus_32k_to_amr_wb | Opus 32k → AMR-WB | 7kHz | |
| | tandem_amr_wb_to_g711_ulaw | AMR-WB → G.711 | 4kHz | |
| | tandem_evs_24400_to_amr_nb_475 | EVS 24.4k → AMR-NB 4.75k | 4kHz | |
| | tandem_evs_24400_to_g711_ulaw | EVS 24.4k → G.711 | 4kHz | |
| | tandem_opus_32k_to_g711_ulaw | Opus 32k → G.711 | 4kHz | |
| |
| --- |
| |
| ## Gate Calibration Reference |
| |
| Empirically derived thresholds from this dataset: |
| |
| | Feature | Source mean | CELP mean | Suggested threshold | Gate use | |
| |---------|------------|-----------|--------------------|---------| |
| | modgd_var | 5.332 | 2.960 | **4.0** | Gate 0: below = CELP confirmed | |
| | bico_f0_f1 | 0.465 | 0.448 | N/A | Channel-agnostic — no gate needed | |
| | codec_cutoff_hz | 4393 | 2336 | **3000** | Provenance: below = NB ceiling | |
| | f1_velocity | 77.3 | 50.7 | **64.0** | Degrades under CELP | |
| | spectral_floor_var | 0.041 | 0.001 | **0.020** | Dead room / digital vacuum gate | |
| | pause_cv | 0.850 | 0.859 | N/A | Preserved — structural, not spectral | |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset please cite: |
|
|
| ``` |
| @dataset{moonscape_ssa_codec_2026, |
| title = {SSA Codec Degradation Study — Acoustic Feature Exports}, |
| author = {Moonscape Software}, |
| year = {2026}, |
| publisher = {HuggingFace}, |
| note = {Export version SSA\_CodecStudy\_v1\_2026} |
| } |
| ``` |
|
|
| and cite the source corpora: |
| - VCTK: Yamagishi et al. (2019) |
| - AMI: Carletta et al. (2005) |
| - CREMA-D: Cao et al. (2014) |
|
|
| --- |
|
|
| ## Licence |
|
|
| **CC-BY-4.0** — source corpora are VCTK (CC-BY-4.0), AMI (CC-BY-4.0), |
| CREMA-D (ODC-BY). Commercial use permitted with attribution. |
| Academic and commercial licences available from Moonscape Software. |
|
|
| *SSA Codec Degradation Study v1 | Moonscape Software | 2026* |
| *Export version: SSA_CodecStudy_v1_2026* |
| *For full data dictionary see SSA_CODEC_STUDY_DATA_DICTIONARY.md* |
|
|