Sound Event Detection
Pretrained models for Sound Event Detection (SED) used in MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing".
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 987 MB
- Versión
- vBest
- Actualizado
- 5 nov 2026
- Fuente
- Hugging Face
Descripción de la fuente
Sound Event Detection — Pretrained Models
Pretrained models for Sound Event Detection (SED) used in MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing".
Models
1. YAMNet (Pretrained Baseline)
- Source: google/yamnet (TensorFlow) / PyTorch reimplementation
- Classes: 521 AudioSet classes
- Usage: Loaded directly from HuggingFace — no checkpoint in this repo
2. AST (Pretrained Baseline)
- Source: MIT/ast-finetuned-audioset-10-10-0.4593
- Architecture: Audio Spectrogram Transformer
- Classes: 527 AudioSet classes
- Usage: Loaded directly from HuggingFace — no checkpoint in this repo
3. Fine-tuned AST (sed_ast_snr_ctl_v2_16k)
- Base model:
MIT/ast-finetuned-audioset-10-10-0.4593 - Fine-tuned on: On-the-fly synthesized binaural audio mixtures (SNR-controlled, 16kHz)
- Classes: 20 target sound classes
- Training: AdamW, OneCycleLR with group-wise learning rates (backbone 1e-5, head 1e-3), 80 epochs
- Checkpoint:
sed_ast_snr_ctl_v2_16k/checkpoints/best.pt
File Structure
.
├── README.md
└── sed_ast_snr_ctl_v2_16k/
├── config.json # Training configuration
└── checkpoints/
└── best.pt # Fine-tuned model weights (~2GB)
Usage
# Fine-tuned AST
from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(
repo_id="ooshyun/sound_event_detection",
filename="sed_ast_snr_ctl_v2_16k/checkpoints/best.pt",
)
config_path = hf_hub_download(
repo_id="ooshyun/sound_event_detection",
filename="sed_ast_snr_ctl_v2_16k/config.json",
)
For training and evaluation code, see ooshyun/sound_event_detection.
Citation
If you use these models, please cite:
MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing"
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 5 nov
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactosed_ast_snr_ctl_v2_16k/checkpoints/best.pt
pt · 987 MB · SHA-256 1a640a4e87be…815d · Hugging Face
Download fileExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: mit tags: - audio - sound-event-detection - audio-spectrogram-transformer - yamnet datasets: - audioset language: - en pipeline_tag: audio-classification --- # Sound Event Detection — Pretrained Models Pretrained models for Sound Event Detection (SED) used in **MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing"**. ## Models ### 1. YAMNet (Pretrained Baseline) - **Source**: [google/yamnet](https://huggingface.co/google/yamnet) (TensorFlow) / PyTorch reimplementation - **Classes**: 521 AudioSet classes - **Usage**: Loaded directly from HuggingFace — no checkpoint in this repo ### 2. AST (Pretrained Baseline) - **Source**: [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) - **Architecture**: Audio Spectrogram Transformer - **Classes**: 527 AudioSet classes - **Usage**: Loaded directly from HuggingFace — no checkpoint in this repo ### 3. Fine-tuned AST (`sed_ast_snr_ctl_v2_16k`) - **Base model**: `MIT/ast-finetuned-audioset-10-10-0.4593` - **Fine-tuned on**: On-the-fly synthesized binaural audio mixtures (SNR-controlled, 16kHz) - **Classes**: 20 target sound classes - **Training**: AdamW, OneCycleLR with group-wise learning rates (backbone 1e-5, head 1e-3), 80 epochs - **Checkpoint**: `sed_ast_snr_ctl_v2_16k/checkpoints/best.pt` ## File Structure ``` . ├── README.md └── sed_ast_snr_ctl_v2_16k/ ├── config.json # Training configuration └── checkpoints/ └── best.pt # Fine-tuned model weights (~2GB) ``` ## Usage ```python # Fine-tuned AST from huggingface_hub import hf_hub_download checkpoint_path = hf_hub_download( repo_id="ooshyun/sound_event_detection", filename="sed_ast_snr_ctl_v2_16k/checkpoints/best.pt", ) config_path = hf_hub_download( repo_id="ooshyun/sound_event_detection", filename="sed_ast_snr_ctl_v2_16k/config.json", ) ``` For training and evaluation code, see [ooshyun/sound_event_detection](https://github.com/ooshyun/sound_event_detection). ## Citation If you use these models, please cite: ``` MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing" ```
Source context: 0 downloads · 0 likes · Pipeline audio-classification · Repo ooshyun/sound_event_detection
