---
pretty_name: ART (Audio Reasoning Tasks)
license: cc-by-4.0
size_categories:
- 1K<n<10K
tags:
- audio
- audio-reasoning
- benchmark
paper:
- https://aclanthology.org/2026.eacl-long.42/
configs:
- config_name: all
  default: true
  data_files:
  - split: test
    path: data/*/test-*.parquet
- config_name: audio_arithmetics
  data_files:
  - split: test
    path: data/audio_arithmetics/test-*.parquet
- config_name: audio_transformation_detection
  data_files:
  - split: test
    path: data/audio_transformation_detection/test-*.parquet
- config_name: cross_recording_language_identification
  data_files:
  - split: test
    path: data/cross_recording_language_identification/test-*.parquet
- config_name: cross_recording_speaker_identification
  data_files:
  - split: test
    path: data/cross_recording_speaker_identification/test-*.parquet
- config_name: selective_text_inference
  data_files:
  - split: test
    path: data/selective_text_inference/test-*.parquet
- config_name: sound_reasoning
  data_files:
  - split: test
    path: data/sound_reasoning/test-*.parquet
- config_name: speech_features_comparison
  data_files:
  - split: test
    path: data/speech_features_comparison/test-*.parquet
- config_name: text_and_sound_reasoning
  data_files:
  - split: test
    path: data/text_and_sound_reasoning/test-*.parquet
- config_name: text_and_temporal_localization_reasoning
  data_files:
  - split: test
    path: data/text_and_temporal_localization_reasoning/test-*.parquet
dataset_info:
- config_name: all
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: audio_arithmetics
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: audio_transformation_detection
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: cross_recording_language_identification
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: cross_recording_speaker_identification
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: selective_text_inference
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: sound_reasoning
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: speech_features_comparison
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: text_and_sound_reasoning
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
- config_name: text_and_temporal_localization_reasoning
  features:
  - name: instance_id
    dtype: string
  - name: task
    dtype: string
  - name: audio
    dtype: audio
  - name: answer
    dtype: string
  - name: question
    dtype: string
  - name: template_id
    dtype: string
  - name: question_id
    dtype: string
  - name: slots
    dtype: string
language:
- en
---

# ART (Audio Reasoning Tasks)

## Dataset Description

**ART** (Audio Reasoning Tasks) is a benchmark for evaluating the audio reasoning capabilities of
multimodal large language models (MLLMs). Unlike benchmarks that evaluate individual audio
capabilities in isolation, **ART** focuses on tasks that require models to combine multiple skills
for understanding and reasoning over audio signals.

The benchmark consists of **9 000 audio samples** spanning **nine audio reasoning tasks**, with
1 000 samples per task and over **30 hours of audio** in total. Each sample contains a spoken
question together with the audio information required to answer it. All questions are formulated so
that the expected answer can be reduced to **Yes** or **No**.

**ART** is fully balanced with respect to both tasks and target labels - each task contains 500
samples with the expected answer `Yes` and 500 samples with the expected answer `No`.

The benchmark was designed around two main principles:

1. A task should require reasoning over multiple properties of the audio signal rather than being
solvable using the output of a single specialized audio-processing module.
2. A task should be approachable by a person without professional training in audio, speech, or
music.

For more details, see the [**ART** paper](https://aclanthology.org/2026.eacl-long.42/).

## Example Usage

The dataset can be loaded using the `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("amu-cai/ART", "all", split="test")
task = load_dataset("amu-cai/ART", "audio_arithmetics", split="test")
```

## Supported Tasks

**ART** contains nine tasks covering different forms of reasoning over speech, environmental sounds,
and other acoustic information.

| Task | Abbreviation | Description |
| ---- | ------------ | ----------- |
| Audio Arithmetics | `AA` | Performing simple arithmetic reasoning based on sounds heard in the recording. |
| Audio Transformation Detection | `ATD` | Determining whether one recording is a transformed version of another. |
| Cross-Recording Language Identification | `CRLI` | Comparing languages spoken across recordings and reasoning about the identified languages. |
| Cross-Recording Speaker Identification | `CRSI` | Comparing speaker identities across recordings. |
| Selective Text Inference | `STI` | Reasoning about spoken content selected according to properties of the speakers. |
| Sound Reasoning | `SR` | Answering questions that require recognizing sounds and reasoning about the objects or events producing them. |
| Speech Features Comparison | `SFC` | Comparing recordings with respect to speech characteristics such as accent. |
| Text and Sound Reasoning | `TSR` | Combining spoken-language understanding with recognition of non-speech sounds. |
| Text and Temporal Localization Reasoning | `TTLR` | Combining spoken-language understanding with temporal or acoustic-scene information. |

For example, tasks may require determining whether two speakers are the same person, comparing the
number of sound events, identifying relationships between languages, or combining the semantic
content of speech with environmental sounds.

## Languages

Most synthesized speech in **ART** is in **English**.

The Cross-Recording Language Identification (`CRLI`) task additionally contains speech in four
languages - Estonian, Finnish, Hungarian, and Polish.

## Dataset Structure

### Data Instances

An example instance from the Audio Arithmetics task:

```python
{
  'instance_id': 'aa_4160014',
  'task': 'audio_arithmetics',
  'audio': {
    'path': None,
    'array': array([ 0.00054932,  0.00054932,  0.00045776, ...,
      -0.00027466, -0.00024414, -0.00045776]),
    'sampling_rate': 16000
  },
  'answer': 'yes',
  'question': 'Are there more bell rings than cat meows?',
  'template_id': 'aa_4',
  'question_id': 'aa_416',
  'slots': '{"n_sound_A": 4, "n_sound_B": 3, "sound_A": "bell", "sound_B": "cat"}'
}
```

### Data Fields

The benchmark provides the information necessary to associate each audio recording with:

- `instance_id` (`string`): Unique identifier of the instance.

- `task` (`string`): Name of the audio reasoning task the instance belongs to.

- `audio` (`audio`): Audio recording containing the spoken question and the audio information
required to answer it.

  - `path` (`string` or `None`): Path associated with the audio file. May be `None` when the audio
  is decoded directly from the dataset.

  - `array` (`array`): Decoded audio waveform.

  - `sampling_rate` (`int`): Sampling rate of the audio waveform, equal to 16 kHz.

- `answer` (`string`): Ground-truth answer to the question, eiter `yes` or `no`.

- `question` (`string`): Textual representation of the question spoken in the audio.

- `template_id` (`string`): Identifier of the template used to generate the question.

- `question_id` (`string`): Identifier of the generated question.

- `slots` (`string`): JSON-encoded dictionary containing the values used to fill the question
template. Its contents depend on the task and template.

### Data Distribution

| Task      | Samples   | Templates | Speakers | Utterances | Sounds | Total duration  |
| --------- | --------: | --------: | -------: | ---------: | -----: | --------------: |
| AA        | 1 000     | 6         | -        | -          | 5      | 3h 46m 10s      |
| ATD       | 1 000     | 4         | -        | -          | 4      | 3h 53m 30s      |
| CRLI      | 1 000     | 6         | 12       | 12         | -      | 3h 32m 01s      |
| CRSI      | 1 000     | 4         | 4        | 8          | -      | 3h 46m 17s      |
| STI       | 1 000     | 4         | 4        | 36         | -      | 3h 09m 47s      |
| SR        | 1 000     | 15        | -        | -          | 20     | 3h 08m 15s      |
| SFC       | 1 000     | 4         | 10       | 3          | -      | 2h 37m 07s      |
| TSR       | 1 000     | 8         | 4        | 16         | 17     | 3h 25m 24s      |
| TTLR      | 1 000     | 4         | 4        | 15         | 8      | 3h 00m 47s      |
| **Total** | **9 000** | **55**    | **22**   | **86**     | **25** | **30h 19m 18s** |

Each task contains exactly **1 000 instances**, consisting of 500 `Yes` answers and 500 `No`
answers.

The complete benchmark therefore contains 4 500 samples for each answer class.

## Dataset Creation

### Task Selection

**ART** was created using a multi-stage procedure.

Candidate tasks were first proposed by speech and natural language processing experts. Tasks were
required to combine multiple audio-processing capabilities and to remain approachable without
professional training.

Tasks involving subjective judgments, such as emotion classification or subjective audio-quality
assessment, were excluded. Tasks that could not be expressed as Yes/No questions were also excluded
to allow evaluation without relying on another language model as a judge.

This process resulted in the nine tasks included in **ART**.

### Task Templates

Instances were generated using task-specific templates. Each template contains slots populated with
values such as sentences, speakers, sounds, or other task-specific attributes.

The use of templates enables systematic generation of examples while maintaining control over the
distribution of tasks and answers.

In total, ART uses **55 templates** across its nine tasks.

### Speech

The spoken questions were synthesized using a common voice to provide consistent prompts across the
benchmark.

Additional utterances required by individual tasks were generated using speech samples selected
from existing speech datasets. English speaker material was derived from
[GLOBE](https://huggingface.co/datasets/MushanW/GLOBE), while the multilingual speech used for
Cross-Recording Language Identification was selected from
[VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli).

The multilingual portion contains speakers of Estonian, Finnish, Hungarian, and Polish.

Speech synthesis was performed using a pipeline based on Voicebox.

### Sounds

Non-speech sounds were manually selected from recordings available under the
**Creative Commons CC0** license in [Freesound](https://freesound.org/).

The selected material included 13 short sounds, four short tunes, and eight background sounds.

Short sound recordings were manually trimmed where necessary to contain a single sound event.

### Audio Processing

After generating the necessary questions, utterances, and sounds, recordings were automatically
composed according to their corresponding task templates.

All recordings were normalized to **−20 dBFS**. Silence between components was manually adjusted to
make the prompts natural and ensure that individual recordings could be distinguished.

Short sounds and tunes concatenated with questions were limited to a maximum duration of five
seconds and faded out where necessary. For tasks involving background audio, the background signal
was attenuated by 20 dB to preserve speech intelligibility.

## Evaluation

**ART** supports a straightforward **Yes/No evaluation protocol**:

```text
Answer the question from the audio.
Answer only "Yes" or "No".
```

Because every instance has a deterministic binary target, models can be evaluated without using an
LLM as a judge.

The paper additionally investigates a descriptive evaluation setting in which models are allowed to
provide unrestricted answers:

```text
Answer the question from the audio.
```

Descriptive answers can subsequently be evaluated manually or using an LLM-based judge.

## Limitations

**ART** is intended to measure whether multimodal models can perform reasoning over audio signals
by combining multiple audio-related capabilities. However, the nine tasks do not represent every
possible form of audio reasoning.

Consequently, strong performance on **ART** should not by itself be interpreted as evidence that a
model has human-level audio reasoning capabilities.

The benchmark also relies substantially on synthesized speech and deliberately uses relatively
clean audio. This design reduces ambiguity and helps isolate reasoning failures from failures
caused by poor acoustic quality, but it does not represent the full range of challenging conditions
encountered in real-world audio.

## Citation Information

The **ART** paper is available in [ACL Anthology](https://aclanthology.org/2026.eacl-long.42/).

If you use **ART** in your research, please cite:

```bibtex
@inproceedings{christop-etal-2026-benchmark,
    title = "A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models",
    author = "Christop, Iwona  and
      Czy{\.z}nikiewicz, Mateusz  and
      Sk{\'o}rzewski, Pawe{\l}  and
      Bondaruk, {\L}ukasz  and
      Kubiak, Jakub  and
      Lewandowski, Marcin  and
      Kubis, Marek",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.eacl-long.42/",
    doi = "10.18653/v1/2026.eacl-long.42",
    pages = "953--983",
    ISBN = "979-8-89176-380-7",
    abstract = "The present benchmarks for testing the audio modality of multimodal large language models concentrate on testing various audio tasks such as speaker diarization or gender identification in isolation. Whether a multimodal model can answer the questions that require reasoning skills to combine audio tasks of different categories cannot be verified with their use. To address this issue, we propose Audio Reasoning Tasks (ART), a new benchmark for assessing the ability of multimodal models to solve problems that require reasoning over audio signal."
}
```