Datasets:
dataset_info:
- config_name: acoustic_atlas
features:
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splits:
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- config_name: control_mel_shuffled
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- config_name: cqt
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- config_name: erb
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- config_name: mel
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- config_name: mel_periodicity
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- config_name: modulation
features:
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splits:
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- config_name: multires_stft
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- config_name: periodicity
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splits:
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- config_name: prosody_voice
features:
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splits:
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- config_name: reassigned
features:
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- config_name: scalogram
features:
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- config_name: source_filter
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- config_name: speech_atlas
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- config_name: stft
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- config_name: waveform
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configs:
- config_name: acoustic_atlas
data_files:
- split: test
path: acoustic_atlas/test-*
- config_name: control_blank_image
data_files:
- split: test
path: control_blank_image/test-*
- config_name: control_mel_shuffled
data_files:
- split: test
path: control_mel_shuffled/test-*
- config_name: control_no_image
data_files:
- split: test
path: control_no_image/test-*
- config_name: cqt
data_files:
- split: test
path: cqt/test-*
- config_name: erb
data_files:
- split: test
path: erb/test-*
- config_name: mel
data_files:
- split: test
path: mel/test-*
- config_name: mel_periodicity
data_files:
- split: test
path: mel_periodicity/test-*
- config_name: modulation
data_files:
- split: test
path: modulation/test-*
- config_name: multires_stft
data_files:
- split: test
path: multires_stft/test-*
- config_name: periodicity
data_files:
- split: test
path: periodicity/test-*
- config_name: prosody_voice
data_files:
- split: test
path: prosody_voice/test-*
- config_name: reassigned
data_files:
- split: test
path: reassigned/test-*
- config_name: scalogram
data_files:
- split: test
path: scalogram/test-*
- config_name: source_filter
data_files:
- split: test
path: source_filter/test-*
- config_name: speech_atlas
data_files:
- split: test
path: speech_atlas/test-*
- config_name: stft
data_files:
- split: test
path: stft/test-*
- config_name: waveform
data_files:
- split: test
path: waveform/test-*
- config_name: waveform_mel
data_files:
- split: test
path: waveform_mel/test-*
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 | speaker category, emotion | CC BY-NC-SA 4.0 |
| FLEURS | language identification | CC BY 4.0 |
| ESC-50 | sound events | CC BY-NC 3.0; per-clip attribution remains applicable |
| Hillenbrand et al. vowels | vowel classification | hosted with permission in an MIT-licensed repository |
| Yali-Lower | Mandarin consonants | GPL 3.0 or later |
| 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.