RMS-AQA: Real-World Multi-Hop Spatial Audio Question Answering Challenge

A spatial audio question answering (SAQA) dataset for real-world domestic environments.

Overview

RMS-AQA is a spatial audio question answering benchmark built from 10-second First-Order Ambisonics (FOA) recordings collected in smart-home environments. The benchmark is organized as a two-stage question-answering task:

1. Stage 1: ground the audible sound events present in the scene.
2. Stage 2: perform complex spatio-temporal reasoning based on the Stage 1 answer as context.

All audio clips are provided in four-channel, 24 kHz, 10-second FOA format, and every question answering (QA) pair is a multiple-choice question (MCQ) generated by GPT-5.5 and paraphrased by Gemini 3.1. The dataset is split into training, validation, and test sets: the training set is fully synthetic, the validation set combines synthetic and recorded examples, and the hidden evaluation set consists entirely of recorded data. Simulated audio is generated with SpatialScaper by convolving dry sound events with measured spatial room impulse responses and the recorded audios are captured with a Zoom H3-VR recorder.

Stage 2 covers six question dimensions:

• Sound counting (SC)
• Spatial location (SL)
• Temporal detection (TD)
• Temporal relation (TR)
• Spatial relation (SR)
• Action prediction (AP)

Dataset Structure

Each record contains a 10-second audio segment and its two-stage MCQ pair. Stage 1 identifies the sound events present in the scene; Stage 2 asks a complex spatio-temporal question conditioned on the Stage 1 context.The train set contains 170K clips and 170K QA pairs and the validation set is made up of 5K audio clips with 6K QA pairs. The hidden test set includes 2K clips with 3K QA pairs, which will be released on Nov. 24, 2026.

QA example

{
  "records": [
    {
      "segment_id": "train-000000.json",
      "qa_pairs": [
        {
          "stage": 1,
          "question": "What types of sound events can be heard in this audio?",
          "answer": "D",
          "options": {
            "A": "The recording includes key drop.",
            "B": "In the audio, you can hear dishwasher.",
            "C": "The clip contains indoor cricket chorus.",
            "D": "The audible sound types are child shouting."
          }
        },
        {
          "stage": 2,
          "question": "If a domestic robot were monitoring this audio, what should its response plan be?",
          "answer": "D",
          "options": {
            "A": "The best next step is to log and continue passive monitoring for dishwasher near the front-left area.",
            "B": "For this clip, the embodied assistant should trigger a home emergency alert, notify, and monitor safely for child shouting near the front-left area.",
            "C": "The appropriate response plan is to trigger a home emergency alert, notify, and monitor safely for dishwasher near the rear area.",
            "D": "Because of the detected sound, the robot should make a non-intrusive check and notify for child shouting near the rear area."
          },
          "category_id": 6,
          "category_name": "Action Prediction"
        }
      ]
    }
  ]
}

Download

With huggingface_hub (recommended):

pip install huggingface_hub
huggingface-cli download RMS-AQA/RMS-AQA --repo-type dataset --local-dir ./RMS-AQA

With Python:

from huggingface_hub import snapshot_download

snapshot_download(repo_id="RMS-AQA/RMS-AQA", repo_type="dataset", local_dir="./RMS-AQA")

Or with git (requires git-lfs):

git clone https://huggingface.co/datasets/RMS-AQA/RMS-AQA

Folder Layout

RMS-AQA/
├── train_QA/                  # training set QA pairs (train_QA.tar.zst)
├── train_audio/               # training set audio shards (train-*.tar.zst)
├── dev_QA/                    # development set QA pairs (dev_QA.tar.zst)
└── dev_audio/                 # development set audio shards (dev-*.tar.zst)

The *_QA archives contain per-sample JSON files in the schema shown above; the *_audio archives contain the corresponding four-channel FOA clips.