| | --- |
| | 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). |
| |
|
