---
license: mit
task_categories:
- audio-classification
- feature-extraction
tags:
- spatial-audio
- audio-encoder-training
- room-acoustics
- 3d-audio
- binaural-sim
- trajectography
language:
- en
pretty_name: Spatial Audio Encoder Training Dataset (SAET)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: metadata.jsonl
---

# Spatial Audio Encoder Training Dataset (SAET)

A high-fidelity synthetic dataset designed for training audio encoders to perceive and reason about 3D soundscapes. The dataset maps binaural/stereo audio cues to precise spatial trajectories and semantic labels.

## 🎧 Dataset Summary
This dataset contains 10-second stereo scenes (44.1kHz) synthesized in a virtual 3D room. Each scene features 1-3 moving sound sources with ground-truth trajectory metadata sampled at 10Hz.

## 📊 Dataset Generation Progress (Current State)
| Stage | Description | Progress | Details |
|-------|-------------|----------|---------|
| **1. Extraction** | Mono event extraction from AudioSet-Strong | ✅ Complete | 224 events extracted from 70/216 segments. |
| **2. Synthesis** | 3D Spatial Scene Synthesis (Target: 10k) | 🔄 ~75% | 7,500+ scenes generated. |
| **3. Reasoning** | QnA Pair Generation | ⏳ Pending | High-level reasoning tasks (7 categories). |

## 📐 Spatial Metadata Specification
Each audio sample is accompanied by a dense JSON metadata file (in `data/scene_metadata/`) and a summary entry in `metadata.jsonl`.

### Coordinate System
- **Origin**: Bottom-left-front corner of the room $[0, 0, 0]$.
- **Room Dimensions**: $10m \times 8m \times 3m$ (Length $\times$ Width $\times$ Height).
- **Listener (Mic) Position**: Fixed at center $[5.0, 2.0, 1.6]$.
- **Azimuth**: $0^\circ$ is directly in front (+Y), $+90^\circ$ is Right (+X), $-90^\circ$ is Left (-X). Range: $[-180^\circ, 180^\circ]$.
- **Distance**: Euclidean distance from the microphone center in meters.

### Motion Dynamics
Sources follow one of five deterministic motion profiles:
- **Static**: Source remains at a fixed 3D point.
- **Approach**: Source moves linearly towards the listener.
- **Recede**: Source moves linearly away from the listener.
- **Lateral**: Source moves across the field of view (e.g., Left-to-Right).
- **Arc**: Source moves in a circular path around the listener, maintaining relatively constant distance but shifting azimuth.

## 🧠 Reasoning Q&A Pairs (Stage 3)
A subset of scenes includes 7 question-answer pairs generated by an LLM (DeepSeek-R1-Distill-Qwen-7B) focusing on:
1. **Lateral Trajectory**: Directional changes (Left-to-Right, Right-to-Left).
2. **Radial Change**: Distance shifts (Approaching, Receding).
3. **Comparative**: Which source is closer/farther?
4. **Temporal**: Entry/Exit timings (Early, Middle, Late).
5. **Relative Motion**: Inter-source spatial relationships.
6. **Natural Perception**: Qualitative descriptions of sound movement.
7. **Choreography**: Overall spatial pattern recognition.

## 🔊 Audio Simulation Details
- **Engine**: PyRoomAcoustics (Image Source Method).
- **Reverberation**: 2nd order reflections simulated with a frequency-independent absorption coefficient of $0.25$.
- **Source Events**: 224 high-variety mono events extracted from 70/216 AudioSet-Strong segments, rigorously filtered for quality (Duration $\geq$ 3.0s, CLAP semantic similarity score $\geq$ 0.45).
- **Format**: 2-channel Stereo, 16-bit PCM, 44.1kHz.

## 🛠️ Data Columns (metadata.jsonl)
| Column | Type | Description |
|--------|------|-------------|
| `audio` | `Audio` | Path to the stereo `.wav` file. |
| `scene_id` | `int` | Unique ID matching the filename. |
| `labels` | `list` | Semantic classes (e.g., `Crowd`, `Siren`, `Engine`). |
| `num_events` | `int` | Number of simultaneous sources in the scene. |
| `motion_types` | `list` | List of motion profiles for each source. |

## 🎯 Use Cases
1. **Spatial Audio Embedding**: Training models like CLAP or Wav2Vec to create embeddings that cluster by spatial location or motion type.
2. **Trajectory Inference**: Predicting the azimuth/distance change of a source over time.
3. **Source Separation**: Decoupling multiple spatialized streams in a reverberant environment.

---
**Reference**: This dataset follows the methodology of "Spatial Audio Question Answering and Reasoning on Dynamic Source Movements" (2024).