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AuraBench

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Question Answering
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AuraBench / README.md
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---
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language:
- en
- de
- es
- fr
- zh
- ja
- ko
- ar
license: other
task_categories:
- question-answering
pretty_name: AuraBench
tags:
- audio
- vision-language
- spectrogram
- benchmark
- any-to-bench
- signal-processing
- exam
---
# AuraBench Dataset Card
## Summary
AuraBench contains 48 canonical audio questions rendered under 16 deterministic
signal-processing image conditions. Every condition uses the same source waveform,
question, options, and deterministic answer key. Three additional controls remove the
image, replace it with a uniform blank image, or permute mel images within each task
family.
The shuffled-image control uses a deterministic semantic derangement within each
compatible choice set: no image remains on its source question, no image crosses to a
different option vocabulary, and every source image's ground-truth label differs from
the target question's answer. Its taker-visible exam identity matches the ordinary Mel
condition, so the model is not told that image labels have been permuted.
Evaluated models receive image and text inputs only. The Any-to-Bench bundles do not
contain audio files, transcripts, source filenames, corpus labels, or descriptive alt
text.
## Tasks
| Family | Questions | Labels |
|---|---:|---|
| Speaker category | 8 | corpus-provided female / male label |
| Language identification | 8 | English, German, Spanish, French, Mandarin Chinese, Japanese, Korean, Arabic |
| Sound-event classification | 8 | dog, rain, footsteps, keyboard, clock, helicopter, siren, glass |
| Phonetic content | 8 | four English vowels and two complete Mandarin aspiration pairs |
| Lexical recognition | 8 | isolated English digits |
| Emotional expression | 8 | happy, sad, angry, fearful, crossed with two actors |
All questions are single-choice and worth one point. Grading is deterministic.
Public question IDs are opaque random identifiers. They carry no family, source, or
ordinal meaning, and display numbers are omitted. Within each family, question order
is shuffled; every question has its own pinned option permutation; and correct-option
positions are counterbalanced for comparable choice sets. The same audited public
layout is used across every image condition to preserve paired evaluation.
## Evaluation integrity
The published Any-to-Bench bundles include `grading.json` so results can be reproduced.
That file must never be staged into the taker's workspace. For agentic CLI takers, a private
HOME, disabled web search, blocked shell network, and Codex `workspace-write` mode are still
insufficient by themselves: `workspace-write` permits reads elsewhere on the host, including
the source bundle and previous run outputs.
The reference AuraBench runner therefore applies an outer OS-level filesystem policy. The
taker can read and write its current ephemeral a2b run root and can read the pinned Codex
runtime, but it cannot read the project directory, other a2b workspaces, mounted volumes, or
network mounts, and cannot write outside its current run root. OS canaries verify these
boundaries before benchmark execution. See `scripts/hermetic-bin/codex` and
`scripts/run_matrix`.
The corrected reference matrix contains 38 condition-effort pairs and 76 model runs, all with
48/48 coverage and zero schema errors. The hidden shuffled-Mel control scores 7/48 and 10/48
for sol low/medium, compared with the former impossible 48/48 result obtained when host answer
keys were readable. Detailed results and SHA-256 digests are in `results/summary.json`.
## Image conditions
Atomic conditions are waveform, linear STFT, mel spectrogram, CQT,
multi-resolution STFT, reassigned spectrogram, wavelet scalogram, ERB cochleagram,
periodicity maps, modulation energy, source/filter tracks, and a prosody/voice-quality
dashboard. Combination conditions are waveform+mel, mel+periodicity, a speech atlas,
and a general acoustic atlas.
All transformations are classical DSP. AuraBench does not use ASR, neural audio
encoders, neural classifiers, learned embeddings, neural denoisers, or neural source
separation.
## Source corpora and licenses
| Corpus | Used for | Upstream license |
|---|---|---|
| [RAVDESS speech](https://zenodo.org/records/1188976) | speaker category, emotion | CC BY-NC-SA 4.0 |
| [FLEURS](https://huggingface.co/datasets/google/fleurs) | language identification | CC BY 4.0 |
| [ESC-50](https://github.com/karoldvl/ESC-50) | sound events | CC BY-NC 3.0; per-clip attribution remains applicable |
| [Hillenbrand et al. vowels](https://github.com/santiagobarreda/hillenbrand_et_al_1995) | vowel classification | hosted with permission in an MIT-licensed repository |
| [Yali-Lower](https://huggingface.co/datasets/ntnu-smil/yali-lower) | Mandarin consonants | GPL 3.0 or later |
| [Free Spoken Digit Dataset](https://github.com/Jakobovski/free-spoken-digit-dataset) | spoken digits | CC BY-SA 4.0 |
The corpus licenses remain applicable to canonical audio and derived images. This
repository's MIT license covers AuraBench code and original metadata only; it does not
replace upstream data licenses. See `data/provenance/audio_manifest.json` for the
source ID, citation, license string, source hash, and canonical hash of every item.
## Preprocessing
Each source is decoded to mono, DC-centered, resampled to 16 kHz with a polyphase
filter, peak-normalized to 0.98, center-cropped only when longer than the family target,
and right-padded with zeros when shorter. Canonical files are PCM16 WAV. Target lengths
are 1 second for phonetic and digit items, 4 seconds for speaker and emotion items, 5
seconds for sound events, and 10 seconds for language identification.
No sample was selected or removed based on a model's performance. FLEURS transcript
fields are deliberately discarded during ingestion.
## Known source properties
- RAVDESS emotional expressions are acted speech rather than spontaneous emotion.
- Speaker-category ground truth is the source corpus's binary metadata label; it is not
a claim about gender identity inferred from voice.
- ESC-50 documents that some upstream clips may contain class-dependent preprocessing.
- Hillenbrand items are `/hVd/` syllables rather than isolated steady-state vowels.
- A representation may be undefined for a signal. Unreliable F0 and formant estimates
remain blank rather than being replaced by a fallback estimate.
## Bundle layout
Each directory under `benchmark/bundles/` is a complete Any-to-Bench bundle containing
`exam.json`, `answer_schema.json`, `grading.json`, `manifest.json`, `aurabench.json`,
and any required image assets. `aurabench.json` records the image-to-question mapping,
including the deliberate permutation used by the shuffled-image control, using only
opaque public IDs. `config/public_layout.yaml` pins the reproducible question and option
permutations and records the answer-shortcut audit score.